Systems and methods of characterizing high risk plaques

ABSTRACT

A method for characterization of coronary plaque tissue data and perivascular tissue data using image data gathered from a computed tomography (CT) scan along a blood vessel, the image information including radiodensity values of coronary plaque and perivascular tissue located adjacent to the coronary plaque, the method comprising quantifying radiodensity in regions of coronary plaque, quantifying, radiodensity in at least one region of corresponding perivascular tissue adjacent to the coronary plaque, determining gradients of the quantified radiodensity values within the coronary plaque and the quantified radiodensity values within the corresponding perivascular tissue, and determining a ratio of the quantified radiodensity values within the coronary plaque and the corresponding perivascular tissue; and characterizing the coronary plaque by analyzing a gradient of the quantified radiodensity values in the coronary plaque and the corresponding perivascular, and/or the ratio of the coronary plaque radiodensity values and the radiodensity values of the corresponding perivascular tissue.

INCORPORATION BY REFERENCE TO ANY PRIORITY APPLICATIONS

This application is a continuation of U.S. patent application Ser. No.17/033,136, filed Sep. 25, 2020, which is a continuation of U.S. patentapplication Ser. No. 16/750,278, filed Jan. 23, 2020, now U.S. Pat. No.10,813,612, which claims the benefit of U.S. Provisional Application No.62/797,024, filed Jan. 25, 2019. Any and all applications for which aforeign or domestic priority claim is identified in the Application DataSheet as filed with the present application are hereby incorporated byreference under 37 CFR 1.57.

TECHNICAL FIELD

The systems and methods disclosed herein are directed to identifyinghigh risk plaques in coronary arteries, and more particularly, tocharacterizing coronary plaque by using three-dimensional (3D) models ofmedical images of coronary arteries to calculate density ratios of thecoronary plaque and surrounding tissues.

BACKGROUND

The heart is a muscle that receives blood from several arteries,including the left circumflex artery (LCX) and the left anteriordescending artery (LAD), both of which branch off from the left mainartery, and the right coronary artery. Coronary artery disease generallyrelates to a constriction or blockage of one of these arteries and mayproduce coronary lesions in the blood vessels providing blood to theheart, such as a stenosis (abnormal narrowing of a blood vessel) orischemia (a deficient supply of blood to the body part due toobstruction of the inflow of arterial blood). As a result, blood flow tothe heart may be restricted. A patient suffering from coronary arterydisease may experience chest pain. A more severe manifestation ofcoronary artery disease may the lead to myocardial infarction, or aheart attack.

Patients suffering from chest pain and/or exhibiting symptoms ofcoronary artery disease may be subjected to one or more tests that mayprovide some indirect evidence relating to coronary lesions. Forexample, noninvasive tests may include electrocardiograms, blood tests,treadmill exercise tests, echocardiograms, single positron emissioncomputed tomography (SPECT) and positron emission tomography (PET).Anatomic data may be obtained noninvasively using coronary computedtomography angiography (CCTA), which uses computed tomography (CT)scanning after an intravenous infusion of an iodinated contrast agent toexamine the arteries that supply blood to the heart and determinewhether they have been narrowed or blocked by plaque buildup. CCTA andCT scanning, as sometimes used herein, may be hereinafter referred to asCT scanning for brevity. Images generated from a CT scan can bereconfigured to create three-dimensional (3D) images that may be viewedon a monitor, printed on film, or transferred to electronic media.Invasive tests can include measuring the fractional flow reserve (FFR)of any given lesion in order to evaluate the functional significance ofthat lesion. FFR is defined as the ratio of the blood pressuredownstream of a lesion to the blood pressure upstream of the lesion athyperemia and requires cardiac catheterization to be measured. Anothercommon invasive test is the invasive coronary angiogram, with heartdisease severity scored through the SYNTAX scoring system, whichinvolves assessing the coronary anatomy using angiograms and answering aseries of questions. A score is generated based on the lesioncharacteristics and responses to the questions to determine the mostappropriate course of treatment. This process is time-consuming anddepends on an individual cardiologist's subjective characterizations ofthe coronary angiograms. Because of this limitation, the SYNAX score issometimes performed by multiple cardiologists to obtain an averagescore, which increases the time and resources required to perform theassessment. Further, as with any invasive medical procedures, FFR andSYNTAX scoring risk adverse effects and unnecessary medical costs.

Although plaque features, such as adverse plaque characteristics (APCs),have been investigated for prognostic value of major adverse cardiacevents using both invasive and noninvasive techniques (such asintravascular ultrasound, optical coherence tomography, and coronarycomputed tomography data), a need exist for methods and systems forpredicting adverse cardiac events by characterizing individual coronaryplaque using noninvasive imaging techniques (atomic image data specificto a patient).

SUMMARY

Coronary artery disease (CAD) is a major cause of morbidity andmortality. Coronary computed tomographic angiography (CCTA, sometimesreferred to simply as CT) has emerged as a non-invasive method forevaluation of CAD. Coronary atherosclerosis is the primary diseaseentity of CAD, with coronary stenosis and ischemia serving as secondaryand tertiary consequences of the atherosclerotic process. The primarymechanisms for heart attack resulting from coronary atherosclerosis arerupture of the plaque, erosion of the plaque, or a calcified nodularprotrusion that punctures the plaque. Coronary atherosclerosis maypresent in many different forms: focal or diffuse, at bifurcations ortrifurcations/along straight segments of the artery; of differentcompositions, for example, plaque can be graded as necrotic core,fibrofatty, fibrous, calcified and densely calcified; and of different3D shapes. Not all coronary plaque will be implicated in adverse cardiacevents, and over-treatment of non-risky plaques can impose unnecessaryhealth risks to patients and may result in unnecessary increased healthcare costs. Herein is described methods and systems of using imagesgenerated by scanning a patient's arteries (e.g., CCTA) to identifycoronary artery plaques that are at higher risk of causing future heartattack or acute coronary syndrome. While it cannot necessarily bedetermined whether these plaques will rupture, erode or protrude, it canbe noted that these plaques will likely be the future culprits of heartattack or acute coronary syndrome (ACS).

Scientific evidence reported to date that examine “high risk plaque”features have focused on plaques alone and have identified certainatherosclerotic plaque features as associated with future risk of heartattack or ACS, such as aggregate plaque volume (APV), low attenuationplaque (LAP), positive arterial remodeling (PR) and napkin ring signs(NRS). Such studies, however, have failed to consider the relationshipbetween the plaque and adjoining vascular structures, most notably thecoronary lumen and the perivascular coronary fat/tissue.

“Radiodensity” as used herein is a broad term that refers to therelative inability of electromagnetic relation (e.g., X-rays) to passthrough a material. In reference to an image, radiodensity values referto values indicting a density in image data (e.g., film, print, or in anelectronic format) where the radiodensity values in the imagecorresponds to the density of material depicted in the image.“Attenuation” as used herein is a broad term that refers to the gradualloss of intensity (or flux) through a medium. “Hypo-attenuation” is aterm that may be used to indicate, in an image, portions of materialshaving low density that appear darker. “Hyper-attenuation” is a termthat may be used to indicate, in an image, portions of materials havinghigh density that appear lighter in the image. Antoniades reported a fatattenuation index, FAI, which could identify high risk plaques bycharacterizing the radiodensity gradient of fat tissue (e.g.,perivascular adipose tissue) near or surrounding such plaque. (See,Antonopoulos et al., “Detecting human coronary inflammation by imagingperivascular fat,” Sci. Transl. Med., Vol. 9, Issue 398, Jul. 12, 2017.)The attenuation density of fat cells closer to the density of water(e.g., Hounsfield unit (HU) density=0) contrast with fat cells of alower HU density (closer to −100), with the former associated with moreinflamed fat cells and achieving higher attenuation due to cholesterolefflux from those cells. This was assumed to identify areas where thereare coronary artery atherosclerotic lesions that are more inflamed.

No study to date, however, has considered the relationship of thedensities of the coronary lumen, the plaque itself and the perivascularcoronary fat. As summarized below and described herein, methods andsystems are described for identifying a coronary plaque that is atincreased susceptibility to be implicated in future ACS, heart attack ordeath. In some embodiments, a ratio method is described where the plaqueserves as the central fulcrum point between the lumen or perivascularcoronary fat.

One innovation includes a method for characterization (e.g., volumetriccharacterization) of coronary plaque using data from images of thecoronary plaque and perivascular tissue gathered from a computedtomography (CT) scan along a blood vessel, the image informationincluding radiodensity values of coronary plaque and perivascular tissuelocated adjacent to the coronary plaque. In some embodiments, theperivascular tissue may include the vessel lumen and/or the perivascularfat. In some embodiments, the method can include creating threedimensional (3D) models from CT images prior to determining radiodensityvalues. In some embodiments, the method can include quantifying, in theimage data, the radiodensity in regions of coronary plaque, quantifying,in the image data, radiodensity in at least one region of correspondingperivascular tissue adjacent to the coronary plaque, determininggradients of the quantified radiodensity values within the coronaryplaque and the quantified radiodensity values within the correspondingperivascular tissue, determining a ratio of the quantified radiodensityvalues within the coronary plaque and the corresponding perivasculartissue, and characterizing the coronary plaque based on at least theradiodensity values and/or ratios. In some examples, the plaque may becharacterized by analyzing one or more of a minimum radiodensity valueof the plaque and/or the perivascular tissue and a maximum radiodensityvalue of the plaque and/or the perivascular tissue. In some embodiments,the perivascular tissue may include at least one of coronary arterylumen, fat or coronary plaque. Such methods are performed by one or morecomputer hardware processors configured to execute computer-executableinstructions on a non-transitory computer storage medium. Such methodsmay include one or more other aspects, or the aspects of the methods maybe characterized in various ways, in different embodiments, some ofwhich are described below.

In some embodiments, the method further comprises receiving, via anetwork, the image data at a data storage component. In someembodiments, the network is one of the Internet or a wide area network(WAN). In some embodiments, the image data from a CT scan includes atleast ten images, or at least 30 images, or more. In some embodiments,the method further includes generating a patient report comprising atleast one of a diagnosis, a prognosis, or a recommended treatment for apatient based on the characterization of the coronary plaque

Adipose tissue (or simply “fat”) is a type of connective tissue thatplays an important role in the functioning of the body by storing energyin the form of lipids and cushioning and insulating the body. It is aloose connective tissue composed mostly of adipocytes, but also maycontain a stromal vascular fraction (SVF) of cells includingpreadipocytes (the predecessor cells to adipocytes), fibroblasts, andvascular endothelial cells, and a variety of immune cells. Quantifyingradiodensity in at least one region of perivascular tissue can includequantifying radiodensity of coronary plaque and adipose tissue in one ormore regions or layers of vascular and/or perivascular tissue. In someembodiments, the radiodensity of the scan information is quantified forwater (e.g., as a control or reference point) in each of one or more ofthe regions of coronary plaque and perivascular tissue. In someembodiments, radiodensity of the scan information is quantified fornecrotic core plaque in the each of one or more regions or layers ofcoronary plaque. In some embodiments, the coronary plaque radiodensityvalues and the perivascular tissue radiodensity values are an averageradiodensity. In some embodiments, the coronary plaque radiodensityvalues and the perivascular tissue radiodensity values are a maximumradiodensity. In some embodiments, the coronary plaque radiodensityvalues and the perivascular tissue radiodensity values are a minimumradiodensity. The quantified radiodensities may be characterized asnumerical values. In some embodiments, the quantified radiodensitiesaccount for CT scan- and patient-specific parameters, including but notlimited to one or more of iodinated contrast agent, contrast type,injection rate, aortic contrast opacification, left ventricular bloodpool opacification, signal-to-noise, contrast-to-noise, tube voltage,milliamps, method of cardiac gating, CT scanner type, heart rate, heartrhythm, or blood pressure.

Such methods may also include reporting the quantified radiodensities ofthe coronary plaque and the perivascular tissue as a gradient of suchradiodensities. In some embodiments, the quantified radiodensities ofthe coronary plaque and the perivascular tissue are determined andreported as a ratio of the slopes of the radiodensity gradients of thecoronary plaque and perivascular tissue adjacent to the coronary plaque.In some embodiments, the quantified (maximum or minimum) radiodensitiesof the coronary plaque and the perivascular tissue determined and eachare reported as the differences in radiodensity values of the coronaryplaque and the perivascular tissue. In some embodiments, the image datais gathered from a CT scan along a length of at least one of a rightcoronary artery, left anterior descending artery, left circumflexartery, aorta, carotid arteries, or, femoral arteries, or theirbranches. In some embodiments, the data is gathered from a CT scan alonga length of a non-coronary reference vessel, which can be, for example,the aorta. The radiodensity of the image data can be expressed in avariety of measurement units. In one example, the radiodensity isquantified in Hounsfield units. In another example, the radiodensity isquantified in absolute material densities, for example, whenmulti-energy CT is performed, which uses spectral data allowingdifferentiation and classification of tissues to obtainmaterial-specific images.

In some embodiments of the method for characterization (e.g., volumetriccharacterization) of coronary plaque, one or more regions (or layers) ofperivascular tissue extend to an end distance from the outer wall of theblood vessel. In some embodiments of the method, one or more regions (orlayers) of the coronary plaque tissue extend to an end distance from theouter wall of the blood vessel, the end distance being the fixeddistance where the radiodensity of adipose tissue (i) reaches a maximumvalue within the plaque, or (ii) increases by a relative percent (e.g.,≥10%); (iii) or changes by a relative percent versus the lowestradiodensity value in the plaque. In some embodiments, the end distancemay be defined as being the fixed distance where the radiodensity ofadipose tissue (i) reaches a minimum value within the scanned anatomicalarea in a healthy vessel, or (ii) drops by a relative percent (e.g.,≥10%); or (iii) drops by a relative percent versus a baselineradiodensity value in a vessel of the same type free of disease. In someembodiments, the baseline radiodensity value is the radiodensityquantified in a layer of adipose tissue lying within a fixed layer orregion surrounding the outer vessel wall, measured by a thickness, areaor volume. In some embodiments, the baseline perivascular tissueradiodensity is the radiodensity quantified for a layer of adiposetissue lying proximal to the outer wall of the blood vessel. In someembodiments, the baseline adipose tissue radiodensity is theradiodensity quantified for water (as a reference or control point) in alayer of adipose tissue lying proximal to the outer wall of the bloodvessel. The baseline radiodensity may be generated in various ways. Insome embodiments, the baseline radiodensity is an average radiodensity.In some embodiments, the baseline radiodensity is a maximumradiodensity. In some embodiments, the baseline radiodensity is aminimum radiodensity. In some embodiments, a baseline coronary plaqueradiodensity value is the average radiodensity quantified in a layer ofcoronary plaque tissue within a fixed layer or region within the plaqueand is measured by a thickness, area or volume. In some embodiments, thebaseline coronary plaque radiodensity is the radiodensity quantified forall coronary plaques in the measured vessels.

In some embodiments, the method may further include determining a plotof the change in quantified radiodensity relative to baselineradiodensity in each of one or more concentric layers of perivasculartissue with respect to distance from the outer wall of the blood vesselup to an end distance, determining the area of the region bound by theplot of the change in quantified radiodensity and a plot of baselineradiodensity with respect to distance from the outer wall of the bloodvessel up to the end distance, and dividing said area by the quantifiedradiodensity measured at a distance from the outer wall of the bloodvessel, wherein the distance is less than the radius of the vessel or isa distance from the outer surface of the vessel above which thequantified radiodensity of adipose tissue drops by more than 5% comparedto the baseline radiodensity of adipose tissue in a vessel of the sametype free of disease. Some embodiments of the method may furthercomprise determining a plot of the change in quantified radiodensityrelative to baseline radiodensity in each of one or more concentriclayers of coronary plaque tissue with respect to distance from the outerwall of the blood vessel up to the inner surface of the plaque,determining the area of the region bound by the plot of the change inquantified radiodensity and a plot of baseline radiodensity with respectto distance from the outer wall of the blood vessel up to the innersurface of the plaque, and dividing said area by the quantifiedradiodensity measured at a distance from the outer wall of the bloodvessel, wherein the distance is less than the radius of the vessel or isa distance from the outer surface of the vessel above which thequantified radiodensity of adipose tissue drops by more than 5% comparedto the baseline radiodensity of adipose tissue in a vessel of the sametype free of disease. In some embodiments, the quantified radiodensityis the quantified radiodensity of adipose tissue in the each of one ormore regions or layers of perivascular tissue or coronary plaque. Insome embodiments, the quantified radiodensity is the quantifiedradiodensity of water in the each of one or more regions or layers ofperivascular tissue. In some embodiments, the quantified radiodensity isan average radiodensity. In some embodiments, the quantifiedradiodensities are a maximum radiodensity. In some embodiments, thequantified radiodensities are a minimum radiodensity.

In some embodiments of the methods described herein, the methods mayfurther comprise normalizing the quantified radiodensity of the coronaryplaque and the perivascular tissue to CT scan parameters (patient- andCT-specific parameters), which include but are not limited to one ormore of iodinated contrast agent, contrast type, injection rate, aorticcontrast opacification, left ventricular blood pool opacification,signal-to-noise, contrast-to-noise, tube voltage, milliamps, method ofcardiac gating, CT scanner type, heart rate, heart rhythm, or bloodpressure. In some embodiments, the methods may also include normalizingthe quantified radiodensity of the coronary plaque-associatedperivascular fat to remote perivascular fat and/or normalizing thequantified radiodensity of the coronary plaque to remote coronaryplaques.

Embodiments of the method may further comprise quantifying other highrisk plaque features, such as remodeling, volume, spotty calcifications,and further characterizing the high risk plaque based on one or more ofhigh risk plaque features. In some embodiments, characterization of thecoronary plaque includes analyzing plaque heterogeneity, specificallythe presence of calcium and non-calcified plaque admixtures. In someembodiments, characterizing the coronary plaque comprises identifyingthe coronary plaque as a high risk plaque if it is prone to beimplicated as culprit lesions in future acute coronary events, based oncomparison with previously classified patient image data which mayinclude image data from scans taken for the same patient and/or imagedata from scans taken from other patients. In some embodiments,characterizing the coronary plaque comprises identifying the coronaryplaque as a high-risk plaque if it is likely to cause ischemia (e.g.,restriction in blood supply to tissue) based on a comparison withpreviously classified patient image data. In some embodiments,characterizing the coronary plaque comprises identifying the coronaryplaque as a high-risk plaque if it is likely to show aberrations inshear stress (e.g., low shear stress) based on a comparison withpreviously classified patient image data.

A vasospasm is the narrowing of the arteries caused by a persistentcontraction of the blood vessels, which is known as vasoconstriction.This narrowing can reduce blood flow. Vasospasms can affect any area ofthe body including the brain (cerebral vasospasm) and the coronaryartery (coronary artery vasospasm). In some embodiments of the method,characterizing the coronary plaque comprises identifying the coronaryplaque as a high risk plaque if it is likely to cause a vasospasm basedon a comparison with previously classified patient image data. In someembodiments, characterizing the coronary plaque comprises identifyingthe coronary plaque as a high risk plaque if it is likely to rapidlyprogress based on a comparisons with previously classified patient imagedata. In some cases, coronary plaque may calcify, hardening bydeposition of or conversion into calcium carbonate or other insolublecalcium compounds. In some embodiments, characterizing the coronaryplaque comprises identifying the coronary plaque as a high risk plaqueif it is likely not to calcify, based on a comparisons with previouslyclassified patient image data. In some embodiments, characterizing thecoronary plaque comprises identifying the coronary plaque as a high riskplaque if it is likely not to respond, regress or stabilize to medicaltherapy based on a comparisons with previously classified patient imagedata. In some embodiments, characterizing the coronary plaque comprisesidentifying the coronary plaque as a high risk plaque if it isprogresses rapidly in volumetric size. In some embodiments,characterizing the coronary plaque comprises identifying the coronaryplaque as a high risk plaque if it is associated with complications atthe time of revascularization (such as by inducing no-reflow phenomenon)based on a comparisons with previously classified patient image data.

Another innovation includes a system for characterization of coronaryplaque tissue (e.g., volumetric characterization) using image datagathered from one or more computed tomography (CT) scans along a bloodvessel, the image information including radiodensity values of coronaryplaque and perivascular tissue located adjacent to the coronary plaque.The system can include a first non-transitory computer storage mediumconfigured to at least store the image data, a second non-transitorycomputer storage medium configured to at least store computer-executableinstructions, and one or more computer hardware processors incommunication with the second non-transitory computer storage medium.The one or more computer hardware processors are configured to executethe computer-executable instructions to at least quantify, in the imagedata, the radiodensities in regions of coronary plaque, and quantify, inthe image data, radiodensities in at least one region of correspondingperivascular tissue adjacent to the coronary plaque, determine gradientsof the quantified radiodensity values within the coronary plaque and thequantified radiodensity values within the corresponding perivasculartissue, determine a ratio of the quantified radiodensity values withinthe coronary plaque and the corresponding perivascular tissue, andcharacterize the coronary plaque by analyzing one or more of thegradients of the quantified radiodensity values in the coronary plaqueand the corresponding perivascular tissue, or the ratio of the coronaryplaque radiodensity values and the radiodensity values of thecorresponding perivascular tissue.

Another innovation includes a non-transitory computer readable mediumincluding instructions that, when executed, cause one or more hardwarecomputer processors of an apparatus to perform a method, the methodincluding quantifying, in the image data, the radiodensities in regionsof coronary plaque. The method may further include quantifying, in theimage data, radiodensities in at least one region of correspondingperivascular tissue adjacent to the coronary plaque, determininggradients of the quantified radiodensity values within the coronaryplaque and the quantified radiodensity values within the correspondingperivascular tissue. The method may further include determining a ratioof the quantified radiodensity values within the coronary plaque and thecorresponding perivascular tissue. The method may further includecharacterizing the coronary plaque by analyzing one or more of thegradients of the quantified radiodensity values in the coronary plaqueand the corresponding perivascular tissue, or the ratio of the coronaryplaque radiodensity values and the radiodensity values of thecorresponding perivascular tissue.

BRIEF DESCRIPTION OF THE DRAWINGS

The disclosed aspects will hereinafter be described in conjunction withthe accompanying drawings, which are incorporated in and constitute apart of this specification, and are provided to illustrate and provide afurther understanding of example embodiments, and not to limit thedisclosed aspects. In the drawings, like designations denote likeelements unless otherwise stated.

FIG. 1 depicts a schematic of an example of an embodiment of a system100 that includes a processing system 120 configured to characterizecoronary plaque.

FIG. 2 is a schematic illustrating an example of a heart muscle and itscoronary arteries.

FIG. 3 illustrates an example of a set of images generated from scanningalong a coronary artery, including a selected image of a portion of acoronary artery, and how image data may correspond to a value on theHounsfield Scale.

FIG. 4A is a block diagram that illustrates a computer system 400 uponwhich various embodiments may be implemented.

FIG. 4B is a block diagram that illustrates computer modules in acomputer system 400 which may implement various embodiments.

FIG. 5A illustrates an example of a flowchart of a process for analyzingcoronary plaque.

FIG. 5B illustrates an example of a flowchart that expands on a portionof the flowchart in FIG. 5A for determining characteristics of coronaryplaque.

FIG. 6 illustrates a representation of image data depicting an exampleof a portion of a coronary artery 665 (sometimes referred to herein as a“vessel” for ease of reference).

FIG. 7 illustrates the same vessel 665 and features of plaque and fat asillustrated in FIG. 6 and further illustrates additional examples ofareas of an artery, and the plaque and/or perivascular fat that is nearan artery, that may be analyzed to determine characteristics of apatient's arteries

FIG. 8 illustrates examples of regions that may be evaluated tocharacterize plaque, the regions including a portion of a coronaryartery and perivascular tissue disposed adjacent to the coronary artery.

FIG. 9 illustrates an example of an overview of a representation ofimage data of a coronary artery (vessel) 905.

FIG. 10 illustrates another view of the representation of image data ofthe coronary artery (vessel) 905 illustrated in FIG. 9, showing examplesof certain features of plaque, perivascular tissue (e.g., fat) and lumenthat can be evaluated to characterize coronary plaque determine healthcharacteristics of a patient's arteries.

FIG. 11 illustrates another example of determining radiodensity valuesof regions of the perivascular fat and plaque to determine metrics, asdescribed herein.

FIG. 12 illustrates a representation of image data showing a coronaryartery 905, plaque 915, and perivascular fat 920 located adjacent to theplaque (as similarly shown in FIG. 11).

FIG. 13 is a table illustrating an example of a set of patientinformation.

FIG. 14 is a table 1400 illustrating an example of a set of scaninformation.

FIG. 15 is a table 1500 illustrating an example of a set of cardiacinformation.

FIG. 16 is an example of a cross section of a coronary artery 1600. Thecoronary artery includes an inside lumen of the artery and an outervessel wall with gradient radiodensities exhibited in the lumen, withinthe plaque and perivascular tissue outside of the vessel.

FIG. 17 is an image showing an example of a longitudinal straightenedrendering of a coronary artery 1708 that shows a buildup of plaquebetween an inner portion and an outer portion of the coronary artery1708. This figure demonstrates the different compartments of lumen,plaque and perivascular tissue.

FIG. 18 is a chart of plots illustrating the compartment areas ofcross-sections of plaque 1801, lumen 1802, and fat 1803 along the lengthof a coronary artery. Different ratios of these compartments can becalculated by area or by summated volume.

FIG. 19 is another chart of plots illustrating the compartment areas ofcross-sections of plaque 1901, lumen 1902, and fat 1903 along the lengthof a coronary artery.

DETAILED DESCRIPTION OF CERTAIN INVENTIVE ASPECTS

Introduction

Disclosed are methods for identification of high-risk plaques usingvolumetric characterization of coronary plaque and perivascular adiposetissue data by computed tomography (CT) scanning. The volumetriccharacterization of the coronary plaque and perivascular adipose tissueallows for determination of the inflammatory status of the plaque by CTscanning. This is of use in the diagnosis, prognosis and treatment ofcoronary artery disease. While certain example embodiments are shown byway of example in the drawings and will herein be described in detail,these embodiments are capable of various modifications and alternativeforms. It should be understood that there is no intent to limit exampleembodiments to the particular forms disclosed, but on the contrary,example embodiments are to cover all modifications, equivalents, andalternatives falling within the scope of example embodiments.

It will be understood that, although the terms first, second, etc. maybe used herein to describe various elements, these elements should notbe limited by these terms. These terms are only used to distinguish oneelement from another. For example, a first element could be termed asecond element, and, similarly, a second element could be termed a firstelement, without departing from the scope of example embodiments. Asused herein, the term “and/or” includes any and all combinations of oneor more of the associated listed items.

It will be understood that when an element is referred to as being“connected” or “coupled” to another element, it can be directlyconnected or coupled to the other element or intervening elements may bepresent. In contrast, when an element is referred to as being “directlyconnected” or “directly coupled” to another element, there are nointervening elements present. Other words used to describe therelationship between elements should be interpreted in a like fashion(e.g., “between” versus “directly between,” “adjacent” versus “directlyadjacent,” etc.).

Spatially relative terms, such as “beneath,” “below,” “lower,” “above,”“upper,” and the like may be used herein for ease of description todescribe the relationship of one component and/or feature to anothercomponent and/or feature, or other component(s) and/or feature(s), asillustrated in the drawings. It will be understood that the spatiallyrelative terms are intended to encompass different orientations of thedevice in use or operation in addition to the orientation depicted inthe figures. The figures are intended to depict example embodiments andshould not be interpreted to limit the intended scope of the claims. Theaccompanying figures are not to be considered as drawn to scale unlessexplicitly noted.

The terminology used herein is for the purpose of describing particularembodiments only and is not intended to be limiting of exampleembodiments. As used herein, the singular forms “a,” “an” and “the” areintended to include the plural forms as well, unless the context clearlyindicates otherwise. It will be further understood that the terms“comprises,” “comprising,” “includes” and/or “including” when usedherein, specify the presence of stated features, integers, steps,operations, elements, and/or components, but do not preclude thepresence or addition of one or more other features, integers, steps,operations, elements, components, and/or groups thereof. In thisspecification, the term “and/or” picks out each individual item as wellas all combinations of them.

Example embodiments are described herein with reference to cross-sectionillustrations that are schematic illustrations of idealized embodiments(and intermediate structures). As such, variations from the shapes ofthe illustrations as a result, for example, of manufacturing techniquesand/or tolerances, are to be expected. Thus, embodiments should not beconstrued as limited to the particular shapes of regions illustratedherein but are to include deviations in shapes that result, for example,from manufacturing. For example, an implanted region illustrated as arectangle will, typically, have rounded or curved features and/or agradient of implant concentration at its edges rather than a binarychange from implanted to non-implanted region. Likewise, a buried regionformed by implantation may result in some implantation in the regionbetween the buried region and the surface through which the implantationtakes place. Thus, the regions illustrated in the figures are schematicin nature and their shapes are not intended to illustrate the actualshape of a region of a device and are not intended to limit the scope ofexample embodiments.

Unless otherwise defined, all terms (including technical and scientificterms) used herein have the same meaning as commonly understood by oneof ordinary skill in the art to which example embodiments belongs. Itwill be further understood that terms, such as those defined in commonlyused dictionaries, should be interpreted as having a meaning that isconsistent with their meaning in the context of the relevant art andshould not be interpreted in an idealized or overly formal sense unlessexpressly so defined herein.

It should also be noted that in some alternative implementations, thefunctions/acts noted may occur out of the order noted in the figures.For example, two figures shown in succession may in fact be executedsubstantially concurrently or may sometimes be executed in the reverseorder, depending upon the functionality/acts involved.

When it is determined that a detailed description related to a relatedknown function or configuration may make the purpose of exampleembodiments unnecessarily ambiguous, the detailed description thereofmay be omitted. Also, terms used herein are defined to appropriatelydescribe example embodiments and thus may be changed depending on auser, the intent of an operator, or a custom. Accordingly, the termsmust be defined based on the following overall description within thisspecification.

In the drawings, the dimensions of layers and regions are exaggeratedfor clarity of illustration. It will also be understood that when alayer (or tissue) is referred to as being “on” another layer or tissue,it can be directly on the other layer or substrate, or interveninglayers may also be present. Further, it will be understood that when alayer is referred to as being “under” another layer, it can be directlyunder, and one or more intervening layers may also be present. Inaddition, it will also be understood that when a layer is referred to asbeing ‘between’ two layers, it can be the only layer between the twolayers, or one or more intervening layers may also be present. Likereference numerals refer to like elements throughout.

Overview of Example Processing System to Characterize Coronary Plaque

This disclosure includes methods and systems of using data generatedfrom images collected by scanning a patient's arteries to identifycoronary artery plaques that are at higher risk of causing future heartattack or acute coronary syndrome. In particular, the characteristics ofperivascular coronary fat, coronary plaque, and/or the coronary lumen,and the relationship of the characteristics of perivascular coronaryfat, coronary plaque, and/or the coronary lumen are discussed todetermine ways for identifying the coronary plaque that is moresusceptible to implication in future ACS, heart attack and death. Theimages used to generate the image data may be CT images, CCTA images, orimages generated using any applicable technology that can depict therelative densities of the coronary plaque, perivascular fat, andcoronary lumen. For example, CCTA images may be used to generatetwo-dimensional (2D) or volumetric (three-dimensional (3-D)) image data,and this image data may be analyzed to determine certain characteristicsthat are associated with the radiodensities of the coronary plaque,perivascular fat, and/or coronary lumen. In some implementations, theHounsfield scale is used to provide a measure of the radiodensity ofthese features. A Hounsfield unit, as is known, represents an arbitraryunit of x-ray attenuation used for CT scans. Each pixel (2D) or voxel(3D) of a feature in the image data may be assigned a radiodensity valueon the Hounsfield scale, and then these values characterizing thefeatures may be analyzed.

In various embodiments, processing of image information may include: (1)determining scan parameters (for example, mA (milliampere), kvP (peakkilovoltage)); (2) determining the scan image quality (e.g., noise,signal-to-noise ratio, contrast-to-noise ratio); (3) measuringscan-specific coronary artery lumen densities (e.g., from a point distalto a coronary artery wall to a point proximal to the coronary arterywall to distal to the coronary artery, and from a central location ofthe coronary artery to an outer location (e.g., outer relative to radialdistance from the coronary artery): (4) measuring scan-specific plaquedensities (e.g., from central to outer, abruptness of change within aplaque from high-to-low or low-to-high) as a function of their 3D shape;and (5) measuring scan-specific perivascular coronary fat densities(from close to the artery to far from the artery) as a function of its3D shape.

From these measurements, which are agnostic to any commonly knownfeatures of ischemia-causing atherosclerosis, we can determine a numberof characteristics, including but not limited to:

-   -   1. A ratio of lumen attenuation to plaque attenuation, wherein        the volumetric model of scan-specific attenuation density        gradients within the lumen adjusts for reduced luminal density        across plaque lesions that are more functionally significant in        terms of risk value    -   2. A ratio of plaque attenuation to fat attenuation, wherein        plaques with high radiodensities are considered to present a        lower risk, even within a subset of plaques considered        “calcified,” where there can be a gradation of densities (for        example, 130 to 4000 HU) and risk is considered to be reduced as        density increases.    -   3. A ratio of lumen attenuation/plaque attenuation/fat        attenuation    -   4. A ratio of #1-3 as a function of 3D shape of atherosclerosis,        which can include a 3D texture analysis of the plaque    -   5. The 3D volumetric shape and path of the lumen along with its        attenuation density from the beginning to the end of the lumen.    -   6. The totality of plaque and plaque types before and after any        given plaque to further inform its risk.    -   7. Determination of “higher plaque risks” by “subtracting”        calcified (high-density) plaques to obtain a better absolute        measure of high risk plaques (lower-density plaques). In other        words, this particular embodiment involves identifying calcified        plaque and excluding it from further analysis of plaque for the        purpose of identifying high risk plaques.

The above listed metrics and others can be analyzed together to assessthe risk of the plaque being implicated in future heart attack, ACS,ischemia or death. This can be done through development and/orvalidation of a traditional risk score or through machine learningmethods. Factors for analysis from the metrics, that are likely to beassociated with heart attack, ACS, ischemia or death, may include: (1) aratio of [bright lumen:dark plaque]; (2) a ratio of [dark plaque:lightfat]; (3) a ratio of [bright lumen:dark plaque:light fat]; and (4) a lowratio of [dark lumen:dark myocardium in 1 vessel area]/[lumen:myocardiumin another vessel area]. Some improvements in the disclosed methods andsystems include: (1) using numerical values from ratios of[lumen:plaque], [plaque:fat] and [lumen:plaque:fat] instead of usingqualitative definitions of atherosclerotic features; (2) using ascan-specific [lumen:plaque attenuation] ratio to characterize plaque;(3) using a scan-specific [plaque:fat attenuation] ratio to characterizeplaque; (4) using ratios of [lumen:plaque:fat circumferential] tocharacterize plaque; and (5) integration of plaque volume and typebefore and after as a contributor to risk for any given individualplaque.

Atherosclerotic plaque features may change over time with medicaltreatment (colchicine and statin medications) and while some of thesemedications may retard progression of plaque, they also have veryimportant roles in promoting the change in plaque. While statinmedications may have reduced the overall progression of plaque they mayalso have actually resulted in an increased progression of calcifiedplaque and a reduction of non-calcified plaque. This change will beassociated with a reduction in heart attack or ACS or death, and thedisclosed methods can be used to monitor the effects of medical therapyon plaque risk over time. Also, this method can also be used to identifyindividuals whose atherosclerotic plaque features or[lumen:plaque]/[plaque:fat]/[lumen:plaque:fat] ratios indicate that theyare susceptible to rapid progression or malignant transformation ofdisease. In addition, these methods can be applied to single plaques orto a patient-basis wherein whole-heart atherosclerosis tracking can beused to monitor risk to the patient for experiencing heart attack(rather than trying to identify any specific plaque as being causal forfuture heart attack). Tracking can be done by automated co-registrationprocesses of image data associated with a patient over a period of time.

FIG. 1 depicts a schematic of an example of an embodiment of a system100 that includes a processing system 120 configured to characterizecoronary plaque. The processing system 120 include one or more servers(or computers) 105 each configured with one or more processors. Theprocessing system 120 includes non-transitory computer memory componentsfor storing data and non-transitory computer memory components forstoring instructions that are executed by the one or more processorsdata communication interfaces, the instructions configuring the one ormore processors to perform methods of analyzing image information. Amore detailed example of a server/computer 105 is described in referenceto FIG. 4.

The system 100 also includes a network. The processing system 120 is incommunication with the network 125. The network 125 may include, as atleast a portion of the network 125, the Internet, a wide area network(WAN), a wireless network, or the like. In some embodiments, theprocessing system 120 is part of a “cloud” implementation, that can belocated anywhere that is in communication with the network 125. In someembodiments, the processing system 120 is located in the same geographicproximity as an imaging facility that images and stores patient imagedata. In other embodiments, the processing system 120 is locatedremotely from where the patient image data is generated or stored.

FIG. 1 also illustrates in system 100 various computer systems anddevices 130 (e.g., of an imaging facility) that are related togenerating patient image data and that are also connected to the network125. One or more of the devices 130 may be at an imaging facility thatgenerates images of a patient's arteries, a medical facility (e.g., ahospital, doctor's office, etc.) or may be the personal computing deviceof a patient or care provider. For example, as illustrated in FIG. 1, animaging facility server (or computer) 130A may be connected to thenetwork 125. Also, in this example, a scanner 130B in an imagingfacility may be connected to the network 125. One or more other computerdevices may also be connected to the network 125. For example, a mobilewireless device including, for example, a tablet, smart phone, watch, orlaptop 130C (or any other mobile computer device), a personal computer130D, and/or and an image information storage system 130E may also beconnected to the network 125, and communicate with the processing system120, and each other, via the network 125.

The information communicated from the devices 130 to the processingsystem 120 via the network 125 may include image information 135. Invarious embodiments, the image information 135 may include 2D or 3Dimage data of a patient, scan information related to the image data,patient information, and other imagery or image related information thatrelates to a patient. For example, the image information may includepatient information including (one or more) characteristics of apatient, for example, age, gender, body mass index (BMI), medication,blood pressure, heart rate, height, weight, race, whether the patient isa smoker or non-smoker, body habitus (for example, the “physique” or“body type” which may be based on a wide range of factors), medicalhistory, diabetes, hypertension, prior coronary artery disease (CAD),dietary habits, drug history, family history of disease, informationrelating to other previously collected image information, exercisehabits, drinking habits, lifestyle information, lab results and thelike. One example of a set of patent information is illustrated in atable 1300 in FIG. 13. In some embodiments, the image informationincludes identification information of the patient, for example,patient's name, patient's address, driver's license number, SocialSecurity number, or indicia of another patient identification. Once theprocessing system 120 analyzes the image information 135, informationrelating to a patient 140 may be communicated from the processing system120 to a device 130 via the network 125. The patient information 140 mayinclude for example, a patient report. Also, the patient information 140may include a variety of patient information which is available from apatient portal, which may be accessed by one of the devices 130.

In some embodiments, image information comprising a plurality of imagesof a patient's coronary arteries and patient information/characteristicsmay be provided from one or more of the devices 130 to the one or moreservers 105 of the processing system 120 via a network 125. Theprocessing system 120 is configured to generate coronary arteryinformation using the plurality of images of the patient's coronaryarteries to generate two-dimensional and/or three-dimensional datarepresentations of the patient's coronary arteries. Then, the processingsystem 120 analyzes the data representations to generate patient reportsdocumenting a patient's health conditions and risks related to coronaryplaque. The patient reports may include images and graphical depictionsof the patient's arteries in the types of coronary plaque in or near thecoronary arteries. Using machine learning techniques or other artificialintelligent techniques, the data representations of the patient'scoronary arteries may be compared to other patients' datarepresentations (e.g., that are stored in a database) to determineadditional information about the patient's health. For example, based oncertain plaque conditions of the patient's coronary arteries, thelikelihood of a patient having a heart attack or other adverse coronaryeffect can be determined. Also, for example, additional informationabout the patient's risk of CAD may also be determined.

FIG. 2 is a schematic illustrating an example of a heart muscle 225 andits coronary arteries. The coronary vasculature includes a complexnetwork of vessels ranging from large arteries to arterioles,capillaries, venules, veins, etc. FIG. 1 depicts a model 220 of aportion of the coronary vasculature that circulates blood to and withinthe heart and includes an aorta 240 that supplies blood to a pluralityof coronary arteries, for example, a left anterior descending (LAD)artery 215, a left circumflex (LCX) artery 220, and a right coronary(RCA) artery 230, described further below. Coronary arteries supplyblood to the heart muscle 225. Like all other tissues in the body, theheart muscle 225 needs oxygen-rich blood to function. Also,oxygen-depleted blood must be carried away. The coronary arteries wraparound the outside of the heart muscle 225. Small branches dive into theheart muscle 225 to bring it blood. The examples of methods and systemsdescribed herein may be used to determine information relating to bloodflowing through the coronary arteries in any vessels extendingtherefrom. In particular, the described examples of methods and systemsmay be used to determine various information relating to one or moreportions of a coronary artery where plaque has formed which is then usedto determine risks associated with such plaque, for example, whether aplaque formation is a risk to cause an adverse event to a patient.

The right side 230 of the heart 225 is depicted on the left side of FIG.2 (relative to the page) and the left side 235 of the heart is depictedon the right side of FIG. 2. The coronary arteries include the rightcoronary artery (RCA) 205 which extends from the aorta 240 downwardalong the right side 230 of the heart 225, and the left main coronaryartery (LMCA) 210 which extends from the aorta 240 downward on the leftside 235 of the heart 225. The RCA 205 supplies blood to the rightventricle, the right atrium, and the SA (sinoatrial) and AV(atrioventricular) nodes, which regulate the heart rhythm. The RCA 205divides into smaller branches, including the right posterior descendingartery and the acute marginal artery. Together with the left anteriordescending artery 215, the RCA 205 helps supply blood to the middle orseptum of the heart.

The LMCA 210 branches into two arteries, the anterior interventricularbranch of the left coronary artery, also known as the left anteriordescending (LAD) artery 215 and the circumflex branch of the leftcoronary artery 220. The LAD artery 215 supplies blood to the front ofthe left side of the heart. Occlusion of the LAD artery 215 is oftencalled the widow-maker infarction. The circumflex branch of the leftcoronary artery 220 encircles the heart muscle. The circumflex branch ofthe left coronary artery 220 supplies blood to the outer side and backof the heart, following the left part of the coronary sulcus, runningfirst to the left and then to the right, reaching nearly as far as theposterior longitudinal sulcus.

FIG. 3 illustrates an example of a set of images generated from scanningalong a coronary artery, including a selected image of a portion of acoronary artery, and how image data may correspond to a value on theHounsfield Scale. As discussed in reference to FIG. 1, in addition toobtaining image data, scan information including metrics related to theimage data, and patient information including characteristics of thepatient may also be collected.

A portion of a heart 225, the LMCA 210, and the LAD artery 215 isillustrated in the example of FIG. 3. A set of images 305 can becollected along portions of the LMCA 210 and the LAD artery 215, in thisexample from a first point 301 on the LMCA 210 to a second point 302 onthe LAD artery 215. In some examples, the image data may be obtainedusing noninvasive imaging methods. For example, CCTA image data can begenerated using a scanner to create images of the heart in the coronaryarteries and other vessels extending therefrom. Collected CCTA imagedata may be subsequently used to generate three-dimensional image modelsof the features contained in the CCTA image data (for example, the rightcoronary artery 205, the left main coronary artery 210, the leftanterior descending artery 215, the circumflex branch of the leftcoronary artery 220, the aorta 240, and other vessels related to theheart that appear in the image data.

In various embodiments, different imaging methods may be used to collectthe image data. For example, ultrasound or magnetic resonance imaging(MM) may be used. In some embodiments, the imaging methods involve usinga contrast agent to help identify structures of the coronary arteries,the contrast agent being injected into the patient prior to the imagingprocedure. The various imaging methods may each have their ownadvantages and disadvantages of usage, including resolution andsuitability of imaging the coronary arteries. Imaging methods which maybe used to collect image data of the coronary arteries are constantlyimproving as improvements to the hardware (e.g., sensors and emitters)and software are made. The disclosed systems and methods contemplateusing CCTA image data and/or any other type of image data that canprovide or be converted into a representative 3D depiction of thecoronary arteries, plaque contained within the coronary arteries, andperivascular fat located in proximity to the coronary arteriescontaining the plaque such that attenuation or radiodensity values ofthe coronary arteries, plaque, and/or perivascular fat can be obtained.

Referring still to FIG. 3, a particular image 310 of the image data 305is shown, which represents an image of a portion of the left anteriordescending artery 215. The image 310 includes image information, thesmallest point of the information manipulated by a system referred toherein generally as a pixel, for example pixel 315 of image 310. Theresolution of the imaging system used to capture the image data willaffect the size of the smallest feature that can be discerned in animage. In addition, subsequent manipulation of the image may affect thedimensions of a pixel. As one example, the image 310 in a digitalformat, may contain 4000 pixels in each horizontal row, and 3000 pixelsin each vertical column. Pixel 315, and each of the pixels in image data310 and in the image data 305, can be associated with a radiodensityvalue that corresponds to the density of the pixel in the image.Illustratively shown in FIG. 3 is mapping pixel 315 to a point on theHounsfield scale 320. The Hounsfield scale 320 is a quantitative scalefor describing radiodensity. The Hounsfield unit scale lineartransformation of the original linear attenuation coefficientmeasurement into one in which the radiodensity of distilled water atstandard pressure and temperature is defined as zero Hounsfield units(HU), while the radiodensity of air at standard pressure and temperatureis defined as −1000 HU. Although FIG. 3 illustrates an example ofmapping pixel 315 of image 310 to a point on the Hounsfield scale 320,such an association of a pixel to a radiodensity value can also be donewith 3D data. For example, after the image data 305 is used to generatea three-dimensional representation of the coronary arteries.

Once the data has been obtained and rendered into a three-dimensionalrepresentation, various processes can be performed on the data toidentify areas of analysis. For example, a three-dimensional depictionof a coronary artery may be segmented to define a plurality of portionsof the artery and identified as such in the data. In some embodiments,the data may be filtered (e.g., smoothed) by various methods to removeanomalies that are the result of scanning or other various errors.Various known methods for segmenting and smoothing the 3D data may beused, and therefore for brevity of the disclosure will not be discussedin any further detail herein.

FIG. 4A is a block diagram that illustrates a computer system 400 uponwhich various embodiments may be implemented. Computer system 400includes a bus 402 or other communication mechanism for communicatinginformation, and a hardware processor, or multiple processors, 404coupled with bus 402 for processing information. Hardware processor(s)404 may be, for example, one or more general purpose microprocessors.

Computer system 400 also includes a main memory 406, such as a randomaccess memory (RAM), cache and/or other dynamic storage devices, coupledto bus 402 for storing information and instructions to be executed byprocessor 404. Main memory 406 also may be used for storing temporaryvariables or other intermediate information during execution ofinstructions to be executed by processor 404. Such instructions, whenstored in storage media accessible to processor 404, render computersystem 400 into a special-purpose machine that is customized to performthe operations specified in the instructions. The main memory 406 may,for example, include instructions that analyze image information todetermine characteristics of coronary features (e.g., plaque,perivascular fat and coronary arteries) to produce patient reportscontaining information that characterizes aspects of the patients healthrelating to their coronary arteries. For example, one or more metricsmay be determined, the metrics including one or more of a slope/gradientof a feature, a maximum density, minimum density, a ratio of a slope ofone feature to the slope of another feature, a ratio of a maximumdensity of one feature to the maximum density of another feature, aratio of a minimum density of a feature to the minimum density of thesame feature, or a ratio of the minimum density of a feature to themaximum density of another feature.

Computer system 400 further includes a read only memory (ROM) 408 orother static storage device coupled to bus 402 for storing staticinformation and instructions for processor 404. A storage device 410,such as a magnetic disk, optical disk, or USB thumb drive (Flash drive),etc., is provided and coupled to bus 402 for storing information andinstructions.

Computer system 400 may be coupled via bus 402 to a display 412, such asa cathode ray tube (CRT) or LCD display (or touch screen), fordisplaying information to a computer user. An input device 414,including alphanumeric and other keys, is coupled to bus 402 forcommunicating information and command selections to processor 404.Another type of user input device is cursor control 416, such as amouse, a trackball, or cursor direction keys for communicating directioninformation and command selections to processor 404 and for controllingcursor movement on display 412. This input device typically has twodegrees of freedom in two axes, a first axis (e.g., x) and a second axis(e.g., y), that allows the device to specify positions in a plane. Insome embodiments, the same direction information and command selectionsas cursor control may be implemented via receiving touches on a touchscreen without a cursor.

Computing system 400 may include a user interface module to implement aGUI that may be stored in a mass storage device as computer executableprogram instructions that are executed by the computing device(s).Computer system 400 may further, as described below, implement thetechniques described herein using customized hard-wired logic, one ormore ASICs or FPGAs, firmware and/or program logic which in combinationwith the computer system causes or programs computer system 400 to be aspecial-purpose machine. According to one embodiment, the techniquesherein are performed by computer system 400 in response to processor(s)404 executing one or more sequences of one or more computer readableprogram instructions contained in main memory 406. Such instructions maybe read into main memory 406 from another storage medium, such asstorage device 410. Execution of the sequences of instructions containedin main memory 406 causes processor(s) 404 to perform the process stepsdescribed herein. In alternative embodiments, hard-wired circuitry maybe used in place of or in combination with software instructions.

Various forms of computer readable storage media may be involved incarrying one or more sequences of one or more computer readable programinstructions to processor 404 for execution. For example, theinstructions may initially be carried on a magnetic disk or solid statedrive of a remote computer. The remote computer can load theinstructions into its dynamic memory and send the instructions over atelephone line using a modem. A modem local to computer system 400 canreceive the data on the telephone line and use an infra-red transmitterto convert the data to an infra-red signal. An infra-red detector canreceive the data carried in the infra-red signal and appropriatecircuitry can place the data on bus 402. Bus 402 carries the data tomain memory 406, from which processor 404 retrieves and executes theinstructions. The instructions received by main memory 406 mayoptionally be stored on storage device 410 either before or afterexecution by processor 404.

Computer system 400 also includes a communication interface 418 coupledto bus 402. Communication interface 418 provides a two-way datacommunication coupling to a network link 420 that is connected to alocal network 422. For example, communication interface 418 may be anintegrated services digital network (ISDN) card, cable modem, satellitemodem, or a modem to provide a data communication connection to acorresponding type of telephone line. As another example, communicationinterface 418 may be a local area network (LAN) card to provide a datacommunication connection to a compatible LAN (or WAN component tocommunicate with a WAN). Wireless links may also be implemented. In anysuch implementation, communication interface 418 sends and receiveselectrical, electromagnetic or optical signals that carry digital datastreams representing various types of information.

Network link 420 typically provides data communication through one ormore networks to other data devices. For example, network link 420 mayprovide a connection through local network 422 to a host computer 424 orto data equipment operated by an Internet Service Provider (ISP) 426.ISP 426 in turn provides data communication services through the worldwide packet data communication network now commonly referred to as the“Internet” 428. Local network 422 and Internet 428 both use electrical,electromagnetic or optical signals that carry digital data streams. Thesignals through the various networks and the signals on network link 420and through communication interface 418, which carry the digital data toand from computer system 400, are example forms of transmission media.

Computer system 400 can send messages and receive data, includingprogram code, through the network(s), network link 420 and communicationinterface 418. In the Internet example, a server 430 might transmit arequested code for an application program through Internet 428, ISP 426,local network 422 and communication interface 418.

The received code may be executed by processor 404 as it is received,and/or stored in storage device 410, or other non-volatile storage forlater execution.

Accordingly, in an embodiment, the computer system 105 comprises anon-transitory computer storage medium storage device 410 configured toat least store image information of patients. The computer system 105can also include non-transitory computer storage medium storage thatstores instructions for the one or more processors 404 to execute aprocess (e.g., a method) for characterization of coronary plaque tissuedata and perivascular tissue data using image data gathered from acomputed tomography (CT) scan along a blood vessel, the imageinformation including radiodensity values of coronary plaque andperivascular tissue located adjacent to the coronary plaque. Executingthe instructions, the one or more processors 404 can quantify, in theimage data, the radiodensity in regions of coronary plaque, quantify inthe image data, radiodensity in at least one region of correspondingperivascular tissue adjacent to the coronary plaque, determine gradientsof the quantified radiodensity values within the coronary plaque and thequantified radiodensity values within the corresponding perivasculartissue, determine a ratio of the quantified radiodensity values withinthe coronary plaque and the corresponding perivascular tissue, andcharacterizing the coronary plaque by analyzing one or more of thegradients of the quantified radiodensity values in the coronary plaqueand the corresponding perivascular tissue, or the ratio of the coronaryplaque radiodensity values and the radiodensity values of thecorresponding perivascular tissue.

Various embodiments of the present disclosure may be a system, a method,and/or a computer program product at any possible technical detail levelof integration. The computer program product may include a computerreadable storage medium (or mediums) having computer readable programinstructions thereon for causing a processor to carry out aspects of thepresent disclosure. For example, the functionality described herein maybe performed as software instructions are executed by, and/or inresponse to software instructions being executed by, one or morehardware processors and/or any other suitable computing devices. Thesoftware instructions and/or other executable code may be read from acomputer readable storage medium (or mediums).

The computer readable storage medium can be a tangible device that canretain and store data and/or instructions for use by an instructionexecution device. The computer readable storage medium may be, forexample, but is not limited to, an electronic storage device (includingany volatile and/or non-volatile electronic storage devices), a magneticstorage device, an optical storage device, an electromagnetic storagedevice, a semiconductor storage device, or any suitable combination ofthe foregoing. A non-exhaustive list of more specific examples of thecomputer readable storage medium includes the following: a portablecomputer diskette, a hard disk, a solid state drive, a random accessmemory (RAM), a read-only memory (ROM), an erasable programmableread-only memory (EPROM or Flash memory), a static random access memory(SRAM), a portable compact disc read-only memory (CD-ROM), a digitalversatile disk (DVD), a memory stick, a floppy disk, a mechanicallyencoded device such as punch-cards or raised structures in a groovehaving instructions recorded thereon, and any suitable combination ofthe foregoing. A computer readable storage medium, as used herein, isnot to be construed as being transitory signals per se, such as radiowaves or other freely propagating electromagnetic waves, electromagneticwaves propagating through a waveguide or other transmission media (e.g.,light pulses passing through a fiber-optic cable), or electrical signalstransmitted through a wire.

Computer readable program instructions described herein can bedownloaded to respective computing/processing devices from a computerreadable storage medium or to an external computer or external storagedevice via a network, for example, the Internet, a local area network, awide area network and/or a wireless network. The network may comprisecopper transmission cables, optical transmission fibers, wirelesstransmission, routers, firewalls, switches, gateway computers and/oredge servers. A network adapter card or network interface in eachcomputing/processing device receives computer readable programinstructions from the network and forwards the computer readable programinstructions for storage in a computer readable storage medium withinthe respective computing/processing device.

Computer readable program instructions (as also referred to herein as,for example, “code,” “instructions,” “module,” “application,” “softwareapplication,” and/or the like) for carrying out operations of thepresent disclosure may be assembler instructions,instruction-set-architecture (ISA) instructions, machine instructions,machine dependent instructions, microcode, firmware instructions,state-setting data, configuration data for integrated circuitry, oreither source code or object code written in any combination of one ormore programming languages, including an object oriented programminglanguage such as Smalltalk, C++, or the like, and procedural programminglanguages, such as the “C” programming language or similar programminglanguages. Computer readable program instructions may be callable fromother instructions or from itself, and/or may be invoked in response todetected events or interrupts. Computer readable program instructionsconfigured for execution on computing devices may be provided on acomputer readable storage medium, and/or as a digital download (and maybe originally stored in a compressed or installable format that requiresinstallation, decompression or decryption prior to execution) that maythen be stored on a computer readable storage medium. Such computerreadable program instructions may be stored, partially or fully, on amemory device (e.g., a computer readable storage medium) of theexecuting computing device, for execution by the computing device. Thecomputer readable program instructions may execute entirely on a user'scomputer (e.g., the executing computing device), partly on the user'scomputer, as a stand-alone software package, partly on the user'scomputer and partly on a remote computer or entirely on the remotecomputer or server. In the latter scenario, the remote computer may beconnected to the user's computer through any type of network, includinga local area network (LAN) or a wide area network (WAN), or theconnection may be made to an external computer (for example, through theInternet using an Internet Service Provider). In some embodiments,electronic circuitry including, for example, programmable logiccircuitry, field-programmable gate arrays (FPGA), or programmable logicarrays (PLA) may execute the computer readable program instructions byutilizing state information of the computer readable programinstructions to personalize the electronic circuitry, in order toperform aspects of the present disclosure.

Aspects of the present disclosure are described herein with reference toflowchart illustrations and/or block diagrams of methods, apparatus(systems), and computer program products according to embodiments of thedisclosure. It will be understood that each block of the flowchartillustrations and/or block diagrams, and combinations of blocks in theflowchart illustrations and/or block diagrams, can be implemented bycomputer readable program instructions.

These computer readable program instructions may be provided to aprocessor of a general purpose computer, special purpose computer, orother programmable data processing apparatus to produce a machine, suchthat the instructions, which execute via the processor of the computeror other programmable data processing apparatus, create means forimplementing the functions/acts specified in the flowchart and/or blockdiagram block or blocks. These computer readable program instructionsmay also be stored in a computer readable storage medium that can directa computer, a programmable data processing apparatus, and/or otherdevices to function in a particular manner, such that the computerreadable storage medium having instructions stored therein comprises anarticle of manufacture including instructions which implement aspects ofthe function/act specified in the flowchart(s) and/or block diagram(s)block or blocks.

The computer readable program instructions may also be loaded onto acomputer, other programmable data processing apparatus, or other deviceto cause a series of operational steps to be performed on the computer,other programmable apparatus or other device to produce a computerimplemented process, such that the instructions which execute on thecomputer, other programmable apparatus, or other device implement thefunctions/acts specified in the flowchart and/or block diagram block orblocks. For example, the instructions may initially be carried on amagnetic disk or solid state drive of a remote computer. The remotecomputer may load the instructions and/or modules into its dynamicmemory and send the instructions over a telephone, cable, or opticalline using a modem. A modem local to a server computing system mayreceive the data on the telephone/cable/optical line and use a converterdevice including the appropriate circuitry to place the data on a bus.The bus may carry the data to a memory, from which a processor mayretrieve and execute the instructions. The instructions received by thememory may optionally be stored on a storage device (e.g., a solid statedrive) either before or after execution by the computer processor.

The flowchart and block diagrams in the Figures illustrate thearchitecture, functionality, and operation of possible implementationsof systems, methods, and computer program products according to variousembodiments of the present disclosure. In this regard, each block in theflowchart or block diagrams may represent a module, segment, or portionof instructions, which comprises one or more executable instructions forimplementing the specified logical function(s). In some alternativeimplementations, the functions noted in the blocks may occur out of theorder noted in the Figures. For example, two blocks shown in successionmay, in fact, be executed substantially concurrently, or the blocks maysometimes be executed in the reverse order, depending upon thefunctionality involved. In addition, certain blocks may be omitted insome implementations. The methods and processes described herein arealso not limited to any particular sequence, and the blocks or statesrelating thereto can be performed in other sequences that areappropriate.

It will also be noted that each block of the block diagrams and/orflowchart illustration, and combinations of blocks in the block diagramsand/or flowchart illustration, can be implemented by special purposehardware-based systems that perform the specified functions or acts orcarry out combinations of special purpose hardware and computerinstructions. For example, any of the processes, methods, algorithms,elements, blocks, applications, or other functionality (or portions offunctionality) described in the preceding sections may be embodied in,and/or fully or partially automated via, electronic hardware suchapplication-specific processors (e.g., application-specific integratedcircuits (ASICs)), programmable processors (e.g., field programmablegate arrays (FPGAs)), application-specific circuitry, and/or the like(any of which may also combine custom hard-wired logic, logic circuits,ASICs, FPGAs, etc. with custom programming/execution of softwareinstructions to accomplish the techniques).

Any of the above-mentioned processors, and/or devices incorporating anyof the above-mentioned processors, may be referred to herein as, forexample, “computers,” “computer devices,” “computing devices,” “hardwarecomputing devices,” “hardware processors,” “processing units,” and/orthe like. Computing devices of the above-embodiments may generally (butnot necessarily) be controlled and/or coordinated by operating systemsoftware, such as Mac OS, iOS, Android, Chrome OS, Windows OS (e.g.,Windows XP, Windows Vista, Windows 7, Windows 8, Windows 10, WindowsServer, etc.), Windows CE, Unix, Linux, SunOS, Solaris, Blackberry OS,VxWorks, or other suitable operating systems. In other embodiments, thecomputing devices may be controlled by a proprietary operating system.Conventional operating systems control and schedule computer processesfor execution, perform memory management, provide file system,networking, I/O services, and provide a user interface functionality,such as a graphical user interface (“GUI”), among other things.

FIG. 4B is a block diagram that illustrates examples of representativeinstructions which may be executed by one or more computer hardwareprocessors in one or more computer modules in a representativeprocessing system (computer system) 120 which may implement variousembodiments described herein. As illustrated in FIG. 1, the processingsystem 120 can be implemented in one computer (for example, a server) orin 2 or more computers (two or more servers). Although the instructionsare represented in FIG. 4B as being in seven modules 450, 455, 460, 465,470, 475, 480, in various implementations the executable instructionsmay be in fewer modules, including a single module, or more modules.

The processing system 120 includes image information stored on a storagedevice 410, which may come from the network 125 illustrated in FIG. 1.The image information may include image data, scan information, and/orpatient data. In this example, the storage device 410 also includesstored plaque information of other patients. For example, the storedplaque information of other patients may be stored in a database on thestorage device 410. In other examples, stored plaque information ofother patients is stored on a storage device that is in communicationwith processing system 120. The other patients' stored plaqueinformation may be a collection of information from one, dozens,hundreds, thousands, tens of thousands, hundreds of thousands, ormillions of patients, or more.

The information for each patient may include characterizations of thatpatient's plaque, such as densities and density gradients of thepatient's plaque, and the location of the plaque relative to theperivascular tissue near or adjacent to the plaque. The information foreach patient may include patient information as illustrated in FIG. 13.For example, the information may include one or more of sex, age, BMI(body mass index), medication, blood pressure, heart rate, weight,height, race, body habitus, smoking history, history or diagnosis ofdiabetes, history or diagnosis of hypertension, prior coronary arterydisease, family history of coronary artery disease and/or otherdiseases, or one or more lab results (e.g., blood test results). Theinformation for each patient may include scan information as illustratedin FIG. 14. For example, the information may include one or more ofcontrast-to-noise ratio, signal-to-noise ratio, tube current, tubevoltage, contrast type, contrast volume, flow rate, flow duration, slicethickness, slice spacing, pitch, vasodilator, beta blockers, reconoption whether it's iterative or filter back projection, recon typewhether it's standard or high resolution, display field-of-view,rotation speed, gating whether it's perspective triggering orretrospective gating, stents, heart rate, or blood pressure. Theinformation for each patient may also include cardiac information asillustrated in FIG. 15. For example, the information may includecharacterizations of plaque including one or more of density, volume,geometry (shape), location, remodeling, baseline anatomy (for diameter,length), compartments (inner, outer, within), stenosis (diameter, area),myocardial mass, plaque volume, and/or plaque composition, texture, oruniformity.

The processing system 120 also includes memory 406, 408, which may bemain memory of the processing system or read only memory (ROM). Thememory 406, 408 stores instructions executable by one or more computerhardware processors 404 (groups of which referred to herein as“modules”) to characterize coronary plaque. The memory 406, 408 will becollectively referred to, in reference to this diagram, as memory 406for the sake of brevity. Examples of the functionality that is performedby the executable instructions are described below.

Memory 406 includes module 450 that generates, from the image datastored on the storage device 410, 2-D or 3-D representations of thecoronary arteries, including plaque, and perivascular tissue that islocated adjacent to or in proximity of the coronary arteries in theplaque. The generation of the 2-D or 3-D representations of the coronaryarteries may be done from a series of images 305 (e.g., CCTA images) isdescribed above in reference to FIG. 3. Once the representation of thecoronary arteries are generated, different portions or segments of thecoronary arteries can be identified for evaluation. For example,portions of interest of the right coronary artery 205, the left anteriordescending artery 215, or the circumflex branch of the left coronaryartery 220 may be identified as areas of analysis (areas of interest)based on input from a user, or based on a feature determined from therepresentation of the coronary artery (plaque).

In module 460, the one or more computer hardware processors quantifyradiodensity in regions of coronary plaque. For example, theradiodensity in regions of coronary plaque are set to a value on theHounsfield scale. In module 465, the one or more computer hardwareprocessors quantify radiodensity of perivascular tissue that is adjacentto the coronary plaque, and quantify radiodensity value of the lumen ofthe vessel of interest. In module 470, the one or more computer hardwareprocessors determine gradients of the radiodensity values of the plaquethe perivascular tissue and/or the lumen. In module 475, the one or morecomputer hardware processors determine one or more ratios of theradiodensity values in the plaque, perivascular tissue, and/or thelumen. Next, in module 480, the one or more computer hardware processorscharacterize the coronary plaque using the gradients of the plaque, theperivascular tissue, and/or the lumen, and/or characterize ratio of theradiodensity values of the coronary plaque to perivascular tissue and/orthe lumen including comparing the gradients and or ratios to a databasecontaining information of other patients plaque gradients and ratios.For example, the gradients and/or the ratios are compared to patientdata that stored on storage device 410. Determining gradients and ratiosof the plaque the perivascular tissue and the lumen are described inmore detail with reference to FIGS. 6-12.

FIG. 5A illustrates an example of a flowchart of a process 500 foranalyzing coronary plaque. At block 505, the process 500 generates imageinformation including image data relating to coronary arteries. Invarious embodiments, this may be done by a scanner 130B (FIG. 1). Atblock 510, a processing system may receive image information via anetwork 125 (FIG. 1), the image information including the image data. Atblock 515, the process 500 generates a 3D representation of the coronaryarteries including perivascular fat and plaque on the processing system.The functionality of blocks 505, 510, and 515, can be performed, forexample, using various scanning techniques (e.g., CCTA) to generateimage data, communication techniques to transfer data over the network,and processing techniques to generate the 3D representation of thecoronary arteries from the image data.

At block 520, the processing system performs a portion of the process500 to analyze the coronary plaque, which is described in further detailin reference to process 550 of FIG. 5B. Additional details of thisprocess to analyze the coronary plaque in particular in reference toFIGS. 6-12.

FIG. 5B illustrates an example of a flowchart that expands on a portionof the flowchart in FIG. 5A for determining characteristics of coronaryplaque. Referring now to FIG. 5B, at block 555, process 550 can utilizethe one or more processors 404 to quantify the radiodensity in regionsof coronary plaque. At block 560, the process 550 can utilize the one ormore processors 404 to quantify, in the image data, radiodensity in atleast one region of corresponding perivascular tissue, meaningperivascular tissue that is adjacent to the coronary plaque. At block565, the process 550 determines gradients of the quantified radiodensityvalues within the coronary plaque and the quantified radiodensity valueswithin the corresponding perivascular tissue. The one or more processors404 can be the means to determine these gradients. At block 570, theprocess 550 may determine a ratio of the quantified radiodensity valueswithin the coronary plaque and the corresponding perivascular tissue.For example, the perivascular tissue that is adjacent to the coronaryplaque. The one or more processors 404 can determine these ratios. Atblock 575, process 550 can utilize the one or more processors 404 tocharacterize the coronary plaque by analyzing one or more of thegradients of the quantified radiodensity values in the coronary plaqueand the corresponding perivascular tissue, or the ratio of the coronaryplaque radiodensity values and the radiodensity values of thecorresponding perivascular tissue. The process 550 can then return backto process 500 as illustrated by the circle A.

Referring again to FIG. 5A, at block 525, the process 500 may comparedetermined information of a particular patient's coronary plaque tostored patient data, for example patient data stored on storage device410. An example of the coronary plaque information of a particularpatient that can be compared to stored patient data is illustrated inFIG. 15. To better understand the particular patient's coronary plaqueinformation, and/or to help determine the particular patient's coronaryplaque information, one or more of the scan information illustrated inFIG. 14 may be used. Also, when comparing a particular patient'scoronary plaque information to previously stored coronary plaqueinformation, one or more characteristics of the patient may be compared,including, for example, one or more of the characteristics of a patientthat are shown in FIG. 13. In some examples, the coronary plaqueinformation of the particular patient being examined may be compared toor analyzed in reference to a patient who has one or more of the same orsimilar patient characteristics. For example, the patient being examinedmay be compared to a patient that has the same or similarcharacteristics of sex, age, BMI, medication, blood pressure, heartrate, weight, height, race, body habitus, smoking, diabetes,hypertension, prior coronary artery disease, family history, and labresults. Such comparisons can be done through various means, for examplemachine learning and/or artificial intelligence techniques. In someexamples, neural network is used to compare a patient's coronary arteryinformation to numerous (e.g., 10,000+) other patients' coronary arteryinformation. For such patients that have similar patient information andsimilar cardiac information (for example, the characteristics shown inFIG. 15), risk assessments of the plaque of the patient being examinedmay be determined.

FIG. 6 illustrates a representation of image data depicting an exampleof a portion of a coronary artery 665 (sometimes referred to herein as a“vessel” for ease of reference). Although FIG. 6 is a two-dimensional(2D) illustration, the image data analyzed may be two dimensional orthree dimensional (e.g., volumetric). FIG. 6 also illustrates examplesof plaque located in the vessel 665 and perivascular fat locatedadjacent to the vessel 665. FIG. 6 further illustrates examples of areas(shown on FIG. 6 as rectangles) that may contain at least a portion ofthe vessel 665, plaque that is in the vessel 665, or perivascular fatthat is adjacent to the vessel 665, where these areas indicate portionsof one or more of the vessel 665, plaque, or perivascular fat that maybe analyzed to determine densities, density gradients, and/or densityratios of the vessel 665, plaque, or perivascular fat to determine oneor more characteristics of a patient's coronary arteries. As illustratedin FIG. 6, the vessel 665 includes a vessel wall 661 and a vessel wall663 which are depicted as boundary lines to provide a graphicalreference of where plaque and fat are located relative to the vessel 665in FIG. 6. The vessel wall 661 and the vessel wall 662 may sometimes bereferred to herein as a first vessel wall and a second vessel wall, orvice versa. The line delineating the vessel walls 661, 663 indicate anouter boundary of the vessel walls 661, 663. In the example illustratedin FIG. 6, all of the fat 625, 640 is located outside of the vesselwalls 661, 663, and all of the plaque 610, 620, 635, 650 is locatedwithin the vessel walls 661, 663.

Plaque can be characterized by its attenuation that is exhibited inimages of the coronary arteries. For example, plaque can becharacterized as being low attenuation plaque, a medium attenuationplaque, a high attenuation plaque, or very high attenuation plaque. Insome cases, these characterizations are not exactly precise and may beaffected by the methods and processes used to collect the images of thecoronary arteries. In some examples, low attenuation plaque can have adensity of about 0 to about 70. In some examples, medium attenuationplaque can have a density of about 70 to about 350 In some examples,high attenuation plaque can have a density of about 350 to about 1000.In some examples, very high attenuation plaque can have a density ofmore than 1000.

FIG. 6 illustrates examples of different types of plaque that may becontained within the vessel walls 661 and 663 of the vessel 665,according to some embodiments. In an example, the plaque may be fibrousplaque 610 having a medium attenuation characteristic, inside the vesselwall 663 and extending towards the interior of the vessel 665. In thisexample the fibrous plaque 610 has other types of plaque adjacent to itwithin the vessel wall 663, and fat disposed outside of the vessel 665juxtaposed or adjacent to the fibrous plaque 610. As illustrated in FIG.6, disposed adjacent to the fibrous plaque 610 is necrotic core plaque615 which has a low attenuation characteristic. FIG. 6 also illustratesan example of a plaque 620 having medium-high attenuationcharacteristics, that is also disposed (or located) adjacent to thefibrous plaque 610. In this example, plaque 620 is also disposedadjacent to necrotic core plaque 615, such that plaque 620 is at leastpartially between the plaque fibrous 610 and the necrotic core plaque615, and the outside boundary of the vessel wall 663. FIG. 6 alsoillustrates an example of a very high attenuation plaque 635 disposedwithin vessel wall 663, but protruding out of the vessel 665 such thatvessel wall 663 extends outward (i.e., away from the center of thevessel 665) around plaque 635. In another example, FIG. 6 illustrates afibrous plaque 650 having a medium attenuation characteristics (i.e.,the attenuation characteristics not being as high as fibrous plaque 610)that is disposed adjacent to and within the vessel wall 661. Asillustrated in FIG. 6, the fibrous plaque 650 generally extends towardsthe center of the vessel 665.

FIG. 6 further illustrates examples of fat that is outside of the vesseladjacent to (or at least in proximity of) the vessel 665. The fatillustrated in FIG. 6 is also in proximity to and/or adjacent to one ormore of the plaques 610, 615, 620, 635. In one example, fat 625 is alonga portion of the vessel wall 623, adjacent to the vessel wall 663, andjuxtaposed to the plaque 620, such that it is adjacent to plaque 620 andin the proximity of plaque 610 and 615. In another example illustratedin FIG. 6, fat 640 as shown outside of the vessel wall 662 and adjacentto plaque 635. In this example, fat 640 at least partially surrounds theportion of plaque 635 extending from the vessel 665 such that a portionof fat 640 is adjacent to the vessel wall 663 on two or more sides ofplaque 635.

Identifying high risk plaques may be dependent upon the interplaybetween contrast attenuation's in the coronary lumen, attenuationpatterns of plaque, and fat attenuation patterns. As mentioned above,FIG. 6 also illustrates boxes indicating examples of areas that includeall or portions vessel 665, perivascular fat, and/or plaque, foranalysis of density gradients and density ratios. In the illustratedboxes in FIG. 6, as well as similar boxes illustrated in FIGS. 7, 10,11, and 12, the boxes are shown as two-dimensional rectangles the coverportions of the representation of the image data depicting a portion ofa coronary artery (vessel 665), plaque, and fat. As will be discussedbelow in reference to FIGS. 11 and 12, the portion of the image datathat is analyzed may be, in some examples, data representing aone-dimensional vector of pixels that is in the rectangular box. Inother words, image data along the line that is in the rectangular box.In other examples, the portion of the image data that is analyzed may bea two dimensional vector of pixels that is in the rectangular box. Inother words, image data contained in two or more adjacent lines that arein the rectangular box. In some cases for the two dimensional vector ofpixels, the image data in the two or more adjacent lines may beprocessed to form a one dimensional vector. For example, the image datain the two or more adjacent lines of image data may be averaged, whichmay help reduce the effect of noise. In some cases, the image data maybe filtered to reduce the effect of noise. In some examples, filteringmay occur before the image data is analyzed for gradient, ratios, slope,minimum density, maximum density, etc.

FIG. 6 illustrates an example of an area, indicated by box 605, wherecontrast attenuation patterns in a proximal portion of the coronarylumen can be analyzed, box 605 extending from a central area of thevessel 665 towards the vessel wall 661. FIG. 6 illustrates anotherexample of an area, indicated by box 652, where contrast attenuationpatterns in a portion of the coronary lumen of vessel 665 can beanalyzed, box 652 extending longitudinally relative to vessel 665 from acentral area of the vessel 665 towards the vessel wall 661. FIG. 6further illustrates an example of an area, indicated by box 662, wherecontrast attenuation patterns of a portion of the lumen, a portion offibrous plaque 610 and plaque 620 can be analyzed, box 662 thus coveringa portion of the vessel 665 and a portion of fibrous plaque 610 andplaque 620. FIG. 6 further illustrates an example of an area indicatedby box 642, where contrast attenuation patterns of a portion of plaque635 and a portion of fat 640 positioned adjacent to plaque 635 can beanalyzed, box 642 extending over a portion of plaque 635 and a portionof fat 640. Information determined by analyzing various aspects of thedensity of coronary artery features (e.g., the lumen, the plaque, and/orthe perivascular fat) can be combined with other information todetermine characteristics of a patient's arteries. In some examples, thedetermined information may include for any of the lumen, plaque orperivascular fat, one or more of a slope/gradient of a feature, amaximum density, a minimum density, a ratio of a slope of the density ofone feature to the slope of the density of another feature, a ratio of amaximum density of one feature to the maximum density of anotherfeature, a ratio of a minimum density of a feature to the minimumdensity of the same feature, a directionality of the density ratios,e.g., a density ratio between features facing one way or direction andfeatures facing in an opposite direction (for example, the radiodensityratio of features facing inwards towards the myocardium and featuresfacing outwards toward the pericardium), or a ratio of the minimumdensity of a feature to the maximum density of another feature. Suchdetermined information may indicate distinct differences in risks ofplaque in a patient. In some examples, determined information (forexample as listed above) may be used with a percentage diameter ofstenosis to determine characteristics of a patient's arteries.Additional information regarding examples of analysis of the attenuationpatterns in the coronary lumen attenuation patterns of plaque, andattenuation patterns of fat are described in reference to FIGS. 9-12.

Still referring to FIG. 6, in an example of the directionality ofradiodensity ratios, the density of a portion of the necrotic coreplaque 615 to the density of a portion of the vessel 665 (e.g.,plaque:vessel inward facing ratio) can be determined and may indicate acertain risk of plaque. In another example of the directionality ofradiodensity ratios, the density of a portion of a portion of the vessel665 to the density of the necrotic core plaque 615 (e.g., vessel:plaqueoutward facing) can be determined and may indicate a certain risk ofplaque. In another example, the density ratio of the necrotic coreplaque 615 to the density of a portion of the vessel 665 (e.g.,plaque:vessel inward facing ratio) can be compared to the density ratioof the necrotic core plaque 615 to the fibrous plaque 620 (e.g.,plaque:plaque outward facing) may indicate a certain risk of plaque. Inother examples, features that are adjacently positioned can be used todetermine inward and/or outward directional radiodensity values that maybe used to indicate a risk associated with plaque. Such ratios mayprovide distinct differences in risk of plaque. Various embodiments ofdirectional radiodensity values and/or directional radiodensity ratioscan be included with any of the other information described herein toindicates plaque risk.

The size of a compartment may be used to also indicate a risk associatedwith plaque. For example, determination of risk associated with a plaquemay be based at least partially on the size of the compartments, suchthat the ratio of the of the radiodensities affects the determination ofrisk and the function of the size of the compartments can also affectthe determination of risk. While the presence of plaque in a patientwhere the ratio of plaque:fat may indicate a high risk plaque, if thereis only a small amount of plaque (e.g., a small compartment of plaque),it would be of risk than if there was a larger compartment of the sameplaque with the same radiodensity ratio of plaque to fat. In oneimplementation, the size (e.g., a volume) of the compartment a feature(e.g., of lumen, plaque, perivascular tissue (fat), and myocardium) canbe determined, and a radiodensity ratio can also be determined, and thenthe ratio can be weighted based on the size of the compartment. Forexample, a large compartment can increase the weight of a ratio to makethe ratio more indicative of a risk associated with the plaque.Similarly, a small compartment can decrease the weight of a ratio tomake the ratio less indicative of a risk associated with the plaque. Inan implementation, only the compartment size of the plaque is used toweight (or adjust) the ratio. In an implementation, the compartment sizeof both of the features that are used in the radiodensity ratio can beused to weight the ratio to determine a resulting risk. In animplementation, the compartment size of one of plaque, lumen,perivascular tissue, or myocardium is used to weight (or adjust) therisk associated with the radiodensity ratio. In an implementation, thecompartment size of more than one of plaque, lumen, perivascular tissue,or myocardium is used to weight the risk associated with theradiodensity ratio. Various embodiments of determining plaque risk usingcompartment size can be included with any of the other informationdescribed herein to indicate plaque risk. Using a compartment size toweight other information, or otherwise adjust a risk associated with theradiodensity ratio, can be done in the examples as described inreference to FIGS. 7-12 and 16-19.

FIG. 7 illustrates the same vessel 665 and features of plaque and fat asillustrated in FIG. 6 and further illustrates additional examples ofareas of an artery, and plaque and/or perivascular fat near the artery,that may be analyzed to determine characteristics of a patient'sarteries. Such areas are indicated in FIG. 7 by rectangular boxes,similar to the illustrations in FIG. 6. Although particular locations ofthe rectangular boxes are illustrated in FIG. 6 and FIG. 7, these areonly examples of areas that may be analyzed. In one example, FIG. 7illustrates box 660 which includes a portion of the vessel 665, aportion of necrotic core plaque 615, a portion of fibrous plaque 610, aportion of plaque 620, and a portion of fat 625. In another example,FIG. 7 illustrates box 655 which includes a portion of the vessel 665, aportion of the fibers plaque 610 a portion of the plaque 620 the portionof the necrotic core plaque 615, and a portion of fat 625. Box 655 may,in some cases, illustrate the general area for analysis due to theexistence of 3 different types of plaque 610, 615, 620, and adjacentlydisposed fat 625. Particular portions of a general area for analysis maybe analyzed to better understand the characteristics formed by adjacentfeatures. For example, FIG. 7 illustrates the general area 665containing box 660 (described above), box 673, which extends across aportion of fibrous plaque 610 and plaque 620, and box 674 which extendsacross a portion of plaque 620 and perivascular fat 625. As anotherexample, FIG. 7 also illustrates another box 672 that extends across aportion of the vessel 655 and necrotic core plaque 615. As a furtherexample, FIG. 7 illustrates box 671 that extends across a portion of thevessel 665 and fat 640 juxtaposed to the vessel 665. As a furtherexample, FIG. 7 illustrates box 670 that extends across a portion of thevessel 665 and plaque 635. Characteristics of a patient's arteries thatcan be analyzed based on these features can include:

-   -   1. A ratio of lumen attenuation to plaque attenuation, wherein        the volumetric model of scan-specific attenuation density        gradients within the lumen adjusts for reduced luminal density        across plaque lesions that are more functionally significant in        terms of risk value    -   2. A ratio of plaque attenuation to fat attenuation, wherein        plaques with high radiodensities are considered to present a        lower risk, even within a subset of plaques considered        “calcified,” where there can be a gradation of densities (for        example, 130 to 4000 HU) and risk is considered to be reduced as        density increases.    -   3. A ratio of lumen attenuation/plaque attenuation/fat        attenuation    -   4. A ratio of #1-3 as a function of 3D shape of atherosclerosis,        which can include a 3D texture analysis of the plaque    -   5. The 3D volumetric shape and path of the lumen along with its        attenuation density from the beginning to the end of the lumen.    -   6. The totality of plaque and plaque types before and after any        given plaque to further inform its risk.    -   7. Determination of “higher plaque risks” by “subtracting”        calcified (high-density) plaques to obtain a better absolute        measure of high risk plaques (lower-density plaques). In other        words, this particular embodiment involves identifying calcified        plaque and excluding it from further analysis of plaque for the        purpose of identifying high risk plaques.

FIG. 8 illustrates an example of an image of a heart 800 and certainregions of a coronary artery 805. In this example, at region 810,contrast attenuation in the proximal portion of the vessel is high. Atregion 820, contrast attenuation in the distal portion of the vessel islow. At region 830, contrast attenuation in the heart muscle is low,that is, in that region of the heart muscle that is close to the distalportion of the vessel. The radiodensity values in these regions may bedetermined and compared. In some examples a ratio of the radiodensityvalues of regions 830 and 820 (i.e., radiodensity values830:radiodensity values 820) and/or the radiodensity values of regions830 and 810 (i.e., radiodensity values 830:radiodensity values 810) canbe used to determine whether there is ischemia or not.

FIG. 9 illustrates an example of an overview of a representation ofimage data of a coronary artery (vessel) 905. In this example, vessel905 includes a lumen walls 910, 911 (the lines indicating the outerboundary of the lumen wall), and plaque 915 which is within the vessel905, that is, plaque 915 is within lumen wall 910, and extends outwardaway from the center of the vessel 905 as well as extending inwardtowards the center of the vessel 905. FIG. 9 also illustratesperivascular fat 920 that is disposed adjacent to plaque 915 and outsideof the vessel 905. That is, lumen wall 910 is between perivascular fat920 and plaque 915. Both the plaque 915 in the perivascular fat dietary920 include contrast attenuation patterns that may be analyzed todetermine characteristics of the coronary artery 905. FIG. 9 alsoincludes spotty calcification 925 located in plaque 915. In thisexample, G1 represents a portion of the perivascular fat 920 and theplaque 915, from the inner surface 930 of the plaque 915 to the outersurface 935 of the perivascular fat 920, where the gradient the gradientof the density of the contrast attenuation may be determined andevaluated, described in more detail in FIG. 10.

FIG. 10 illustrates another view of the representation of image data ofthe coronary artery (vessel) 905 illustrated in FIG. 9, showing examplesof certain features of plaque, perivascular tissue (e.g., fat) and lumenthat can be evaluated to characterize coronary plaque determine healthcharacteristics of a patient's arteries. Plaque 915 and perivascular fat920 that were shown in FIG. 9 are also shown in FIG. 10. The image datain FIG. 10 demonstrates a gradient the transitions from lighter densityvalues to darker density values along a line in the image data thatextends from an edge of the plaque 915, that is closest to the center ofthe vessel 905, to the edge 935 of the perivascular fat 920.

The density of portions of the image data depicting the perivascular fat920, plaque 915, and/or the lumen of the vessel 905 may be evaluated tocharacterize coronary plaque into determine health characteristics of apatient's arteries. As mentioned above, information determined byanalyzing various aspects of the density of the lumen, the plaque,and/or the perivascular fat can include, but is not limited to, one ormore of a slope/gradient of a feature, a maximum density, minimumdensity, a ratio of a slope of one feature to the slope of anotherfeature, a ratio of a maximum density of one feature to the maximumdensity of another feature, a ratio of a minimum density of a feature tothe minimum density of the same feature, or a ratio of the minimumdensity of a feature to the maximum density of another feature. Any ofthis information can be combined with other information to determinecharacteristics of a patient's arteries.

FIG. 10 illustrates several examples of regions of features in imagedata that may be evaluated, other regions of evaluation may also beselected in other examples. A first example is a region 931 ofperivascular fat 920 delineated by a rectangular box indicatingperivascular fat 920. Region 931 extends from an edge of theperivascular fat region 920 to the plaque 915, and the image dataevaluated may be in one or more dimensions (for example two dimensions).Another example is a region 941 that extends across plaque 915. Region941 is adjacent to the perivascular fat region 931 on one side, andadjacent to lumen region 939 on the opposite side. Lumen region 939extends from plaque 915 across a portion of the vessel 905. In thisconfiguration, perivascular fat region 931, plaque region 941, and lumenregion 939, are aligned and span the lumen of the vessel 905, the plaque915, and the perivascular fat region 920. The densities of the imagedata in a portion of, or all of, these regions may be evaluated relatingto their maximum density, minimum density, gradient, or ratio of one ofthese characteristics, as described herein.

In another example, plaque—lumen region 937 is delineated by rectangularbox that extends from the lumen wall 911 across the vessel 905 andacross the plaque 915. Plaque—lumen region 937 represents a twodimensional set densities of image data that all, or portion of, may beevaluated.

In another example, as illustrated in FIG. 10, perivascular fat—plaqueregion 933 is an evaluation region delineated by retainer boxes thatextends from the edge 930 of the plaque 915 to the edge 935 of theperivascular fat 920. This example illustrates that in some cases, twoor more adjacent vectors (or “lines”) of image data across features inthe image data that may include one or more features (e.g., fat, plaque,lumen) may be evaluated. Evaluation of two or more adjacent vectors ofimage data may result in more robust metrics that are less affected bynoise in the image data.

In another example, perivascular fat—plaque region 938 is delineated bya rectangular box that extends from the edge 930 of the plaque 915, thatextends into the vessel 905, to the edge 935 of the perivascular fat 920disposed distal to the plaque 915. Perivascular fat—plaque region 938represents a one dimensional set of densities of image data that all, ora portion of, may be evaluated. As an example of metrics of the featuresdepicted in FIG. 10 (using mere examples of radiodensity values), theslope of the gradient of the image density values in the plaque 915 inthe perivascular fat—plaque region 938 may be −3, the maximum density ofthe plaque 915 may be 98 and the minimum density of the plaque 915 maybe −100. The slope of the gradient of the perivascular fat 920 and theperivascular fat—plaque region 938 may be −5, the maximum density of theperivascular fat 920 in the perivascular fat—plaque region 938 may be180, and a minimum density of the perivascular fat 920 in theperivascular fat—plaque region 938 may be 102. Other metrics of theperivascular fat—plaque region 938 may include: a ratio of the slopes ofthe plaque 915 to the perivascular fat 920 {−3/−5}, a ratio of themaximum density of the plaque 915 to the maximum density of theperivascular fat 920 {98/180}; a ratio of the minimum density of theplaque 915 to the minimum density of the perivascular fat 920{−100/102}; a ratio of the minimum density of the plaque 915 to themaximum density of the perivascular fat 920 {−100/180}; a ratio of themaximum density of the plaque 915 and the minimum density of theperivascular fat 920 {98/102}; and the gradient across the entireperivascular fat—plaque region 938 (e.g., −4).

FIG. 11 illustrates another example of determining radiodensity valuesof regions of the perivascular fat and plaque to determine metrics, asdescribed herein. FIG. 11 shows coronary artery 905, coronary plaque 915located in a lumen wall 910 of the coronary artery 905, and perivascularfat 920 located outside of the lumen wall 910 and adjacent to thecoronary plaque 915, similarly as shown in FIG. 10.

FIG. 11 further illustrates an example of two regions where radiodensitydata that may be evaluated to characterize the plaque. The two regionsinclude a first region (or a perivascular fat 1131 region) in theperivascular fat 920 and a second region (or a plaque region) 1141,adjacent to the first region 1131 and in the coronary plaque 915. Inthis example, the coronary artery 905 and the lumen wall 910 aredepicted as being aligned generally vertically on the page. The plaque915 and the fat 920 are illustrated as extending laterally from theartery 905 (e.g., to the left relative to the figure orientation). Theplaque 915 is within the lumen wall 910 of the artery 905, such that thelateral extent of the lumen wall 910 is shown as coincident with theleftmost boundary 950 of the plaque 915. Dashed line 1125 indicates thealignment of the coronary artery 905 at this location, and in thisexample indicates a center line of the artery 905 which is aligned withthe artery 905 at this location.

As illustrated in FIG. 11, the fat region 1131 and the plaque region1141 represent areas of radiodensity information (e.g., 3D or 2D) ofimage data, generated from one or more images, that are evaluated tocharacterize plaque 915. As described above, in some examples, one ormore images can be used to generate a 3D data set that represents acoronary artery, plaque in the artery, and perivascular tissue that islocated near or adjacent to the artery and/or the plaque. Once a dataset is generated, it can be used to characterize the relationshipbetween one or more of plaque, perivascular tissue and lumen tissue. Insome embodiments of evaluating plaque, the data set is used as a 3D dataset (which may also be referred to as a 3D model). In some embodimentsof evaluating plaque, the data set is used as a 2D data set whereinformation in the data set is looked at in XY (2D) region. In theexample illustrated in FIG. 12, the image data can present 2D or 3Ddata.

In some embodiments of evaluating plaque, a region of radiodensityvalues can be used that are along a line in the image data, whether thedata evaluated is a 2D or 3D representation of an artery. A regionradiodensity values along a line can be referred to as a “linearregion.” The linear region may indicate data that includes, or goesacross, part or all of one or more types of tissues, for example,plaque, perivascular tissue, and/or lumen tissue of the coronary artery.That is, the region may be referred to as indicating a portion of one ormore type of tissue (e.g., plaque, perivascular tissue, and/or lumentissue of the coronary artery) which indicates that some information inthe data set is included in the particular region. The radiodensity datain a linear region is a vector with dimensions 1×n, where n representsthe number of discrete points of radiodensity data along the vector.Some examples of regions which may be linear regions are illustrated anddescribed in FIG. 10. For example, lumen region 939 (FIG. 10) whichincludes part of the vessel 905 tissue, coronary plaque region 941 whichincludes a portion of the coronary plaque 915, perivascular fat region931 which includes a portion of the perivascular fat 920, perivascularfat-plaque region 935 which includes a portion of the perivascular fat920 and the plaque 915, and plaque-lumen region 937 which includes aportion of the plaque 915 and the lumen of the vessel 905.

In the example illustrated in FIG. 11, the plaque region 1141 delineatesa portion of the plaque 915 and extends in a lateral (or substantiallylateral) direction relative to the alignment of the coronary artery asindicated by centerline 1125 of the vessel 905. The plaque region 1141extends from a proximal end 1115 that is closest to the center of theartery 905, to a distal end 1120 that extends laterally away from thecenter line 1125 of the artery 905. The perivascular fat region 931extends from a proximal end 1105 that is closer to the artery 905 to adistal end 1110, farthest from the artery 905. Also shown in FIG. 11,the perivascular fat region 1131 is aligned (or substantially aligned)with the plaque region 1141. The proximal end 1105 of the perivascularfat region 1131 is near, or adjacent to, the distal end 1120 of theplaque region 1141.

Radiodensity data in the perivascular fat region 931 is represented byradiodensity values 951. Radiodensity data in the plaque region 941 isrepresented by radiodensity values 961. These radiodensity values 951,961 may be in Hounsfield Units, described above. Once the regions 1131,1141 are determined, and the radiodensity values in the regions aredetermined, the radiodensity values 951 representing the density of aportion the perivascular fat 920 and the radiodensity values 961representing the radiodensity values of a portion of the plaque 915 maybe analyzed to determine characterizations of the plaque and help assessits risk.

Analysis of the radiodensity values of each linear region can beperformed to determine metrics that indicate the radiodensity values ina region, or the relationship of the cardio density values in one regionas compared to another region. The plaque can be characterized byanalyzing metrics that are determined by one or more of the maximumdensity, minimum density, and/or the slope of the gradients (sometimesreferred to simply as “slope” for ease of reference) for a region orseveral regions, for example, adjacent regions. In some examples,determining metrics can include determining one or more of a maximumdensity of the radiodensity values, a minimum density of theradiodensity values, and/or a slope of the gradient of the radiodensityvalues., one or more of a slope/gradient of a feature, a maximumdensity, minimum density, a ratio of a slope of one feature to the slopeof another feature, a ratio of a maximum density of one feature to themaximum density of another feature, a ratio of a minimum density of afeature to the minimum density of the same feature, or a ratio of theminimum density of a feature to the maximum density of another feature.

In one particular example, referring to FIG. 11, the plaque 915 may becharacterized by analyzing one of more of the maximum density, minimumdensity, and slope of the radiodensity values in the perivascular fatregion 1131 and the adjacent plaque regions 1141. For example, in theillustrated examples of radiodensity values in regions 1131, 1141, themaximum and minimum density of the perivascular fat 920 is 120 and 34,respectively, and the maximum and minimum density of the plaque 915 is30 and −79 respectively. The gradient of the radiodensity values inplaque region 1141 is −2. The gradient the cardio density values inperivascular fat region 1131 is −3. The determined metrics may include,for example:

(a) a ratio of the gradient of the perivascular fat region 1131 and thegradient of the plaque region 1141: {−2:−3};

(b) a ratio of the maximum density of the perivascular fat region 1131and the maximum density of the plaque region 1141: {120:30};

(c) a ratio of the minimum density of the perivascular fat region 1131and the minimum density of the plaque region 1141: {34:−79};

(d) a ratio of the minimum density of the perivascular fat region 1131and the maximum density of the plaque region 1141: {34:30}:

(e) a ratio of the maximum density of the perivascular fat region 1131and the minimum density of the plaque region 1141: {120:−79}; and

(f) the gradient from the proximal end 1115 of the plaque region 1141(e.g., the inner surface of the plaque 915) to the distal end 1110 ofthe perivascular fat region 1131 (e.g., the outer surface of theperivascular fat 920): −2.

A maximum or a minimum density of the radiodensity values in a regionsmay be determined several ways, according to various embodiments. In oneexample, simply the maximum/minimum radiodensity value can be selectedas the maximum or minimum value. However, some data sets may includeoutliers that indicate erroneous data. If it can be determined that anoutlying radiodensity value is in fact erroneous (e.g., usingstatistical methods), then the outlying value may be deleted from theanalysis, or corrected if possible. Outliers may be due to randomvariation or may indicate something scientifically interesting. In anyevent, we typically do not want to simply delete the outlyingobservation. However, if the data contains significant outliers, robuststatistical techniques or alternatively, imaging techniques, can be usedto filter the image data to improve the accuracy of the metrics.

FIG. 12 illustrates a representation of image data showing a coronaryartery 905, plaque 915, and perivascular fat 920 located adjacent to theplaque (as similarly shown in FIG. 11). FIG. 12 also illustrates aperivascular fat region 1231 and the perivascular fat 920, and a plaqueregion 1241 in the plaque 915. The perivascular fat region and 1231 andthe plaque region 1241 differ from the fat region 1131 and the plaqueregion 1141 illustrated in FIG. 11 in that the radiodensity values inthese regions are in the form of a two dimensional vector. That is, ifeach of the regions 1231, 1241 contains rows (e.g., lateral withreference to the page) and columns (e.g., vertical with reference to thepage) of cardio representing the image data in regions 1231, 1241,density values in regions 1231, 1241 includes two or more adjacent rowsof radiodensity values. In one example, the two or more adjacent rows ofradiodensity values may be used to generate minimum and maximum densityvalues, for example, by taking the maximum and minimum density valuefrom any of the two or more rows. In another example, the two or moreadjacent rows of radiodensity values may be used to generate minimum andmaximum density values by averaging the information in the two or morerows, for example, by averaging the maximum radiodensity values in eachof the rows to determine a maximum radiodensity value for the region,and by averaging the minimum radiodensity values in each of the rows todetermine a minimum cardio density value for the region. Similarly, thegradient of the radiodensity values in each of the regions may becalculated based on the radiodensity values in two or more rows. Forexample the gradient of the radiodensity value in each row of the regionmay be calculated, and the gradient can be determined by averaging eachof the calculated gradient values. It is contemplated to use otherstatistical techniques to average multiple radiodensity values in aregion to determine characteristics and metrics of the region. Suchtechniques may be particularly useful to minimize the effect of noise(inaccurate data) in a region.

FIG. 13 is a table 1300 illustrating an example of a set of patientinformation. In this example, the table 1300 includes two columns, afirst column 1305 labeled “Item” and a second column 1310 labeled“Importance or Value.”

In this example, the items of patient information in the first column1305 includes information of a patient's sex, age, BMI, medication,blood pressure, heart rate, weight, height, race, body habitus, smoking,diabetes, hypertension, prior CAD, family history, and labs. In otherexamples, more or fewer items may be included, and/or different itemsmay be included.

The second column 1310 can include a numeric assessment of theimportance or value of each item in the first column 1305. The numericassessment can be provided by a risk score and used to bias the analysisbased on one or items that are deemed to be more important. In someexamples, each item has the same assigned value. In other examples,different values can be assigned to one or more of the items. In someexample, the values can be normalized to add up to 1.0, or 100%, oranother value.

FIG. 14 is a table 1400 illustrating an example of a set of scaninformation. The table 1400 includes a first column 1405 labeled “Item”listing scan related items, and a second column 1410 labeled “Importanceor Value.” In this example, the items of scan information in the firstcolumn 1405 includes contrast to noise ratio, signal-to-noise ratio,tube current, tube voltage, contrast type, contrast volume, flow rate,flow duration, slice thickness, slice spacing, pitch, vasodilator, betablockers, recon option whether it's iterative or filter back projection,recon type whether it's standard or high resolution, displayfield-of-view, rotation speed, gating whether it's perspectivetriggering or retrospective gating, stents, heart rate, or bloodpressure. In other examples, more or fewer items may be included, and/ordifferent items may be included.

The second column 1410 can include a numeric assessment of theimportance or value of each item in the first column 1405. The numericassessment can be provided by a risk score and used to bias the analysisbased on one or items that are deemed to be more important. In someexamples, each item has the same assigned value. In other examples,different values can be assigned to one or more of the items. In someexample, the values can be normalized to add up to 1.0, or 100%, oranother value.

FIG. 15 is a table 1500 illustrating an example of a set of cardiacinformation. The table 1500 includes a first column 1505 labeled “Item”listing scan related items, and a second column 1510 labeled “Importanceor Value.” In this example, the items of cardiac information in thefirst column 1504 includes contrast to density, volume, geometry—shape,location, remodeling, baseline anatomy (for diameter, length),compartments (inner, outer, within), stenosis (diameter, area),myocardial mass, plaque volume, plaque composition, texture, oruniformity. In other examples, more or fewer items may be included,and/or different items may be included.

The second column 1510 can include a numeric assessment of theimportance or value of each item in the first column 1505. The numericassessment can be provided by a risk score and used to bias the analysisbased on one or items that are deemed to be more important. In someexamples, each item has the same assigned value. In other examples,different values can be assigned to one or more of the items. In someexamples, the values can be normalized to add up to 1.0, or 100%, oranother value.

FIG. 16 is an example of a cross section of a coronary artery 1600. InFIG. 16 illustrates the inside lumen wall 1606 of the artery 1600 havingan interior portion 1602 and the outer vessel wall 1608 with gradientradiodensities exhibited in the lumen within the plaque 1604 between thelumen wall 1606 and perivascular tissue 1620 outside of the vessel. Theline 1612 indicates a line through a diameter of the artery 1600.

FIG. 17 is an image showing an example of a longitudinal straightenedrendering of a coronary artery 1708 that shows a buildup of plaquebetween an inner portion and an outer portion of the coronary artery1708. As illustrated in FIG. 17, the coronary artery 1708 includes aninner lumen 1710 having a cavity 1702 within the inner lumen 1710 fortransporting blood. The coronary artery 1708 also includes an outervessel 1706 extending from the left side (relative to the orientation ofFIG. 17) of the coronary artery 1708. Plaque 1704 buildup is between theouter vessel 1706 and the inner lumen 1710. This figure demonstrates thedifferent compartments of the lumen, the plaque and perivascular tissueoutside of the inner lumen 1710 and plaque 1702.

FIG. 18 is a chart of plots illustrating the compartment areas ofcross-sections of plaque 1801, lumen 1802, and fat 1803 along the lengthof a coronary artery. Such plots 1801, 1802, 1803 can be generated forthe left and/or right coronary artery. A plurality of cross-sectionalareas of plaque of a coronary artery can be determined along a length ofthe coronary artery. The distance from the ostium of each plaque, lumen,and fat cross-section can also be determined. The ratios ofcross-sections of the plaque, lumen and fat at one or more portions ofone or more coronary arteries may be indicative of a patient's riskassociated with plaque. FIG. 18 illustrates an example of plots of theplaque cross-sections 1801, lumen cross-sections 1802, and fatcross-sections 1803, where the distance from the ostium of the plaque,lumen, and fat cross-sections 1801, 1802, 1803 are plotted on thex-axis, and area of the respective plaque, lumen, and fat cross-sections1801, 1802, 1803 are plotted on the y-axis. In some embodiments, thedistance scale along the x-axis can be in millimeters, although otherscales can be used in other embodiments. In some embodiments, the areasof the cross-sections can be in mm², although another area unit ofmeasurement can be used in other embodiments. Ratios of thesecompartments along portions of a coronary artery can be calculated basedon one or more or the plaque cross-sections 1801, the lumencross-sections 1802, and the fat cross-sections 1803 at one or morecorresponding distances from the ostium, and the calculated ratios canbe included in a generated display or in a report.

In the example illustrated in FIG. 18, along the entire distance fromthe ostium, the cross-sections of the lumen 1802 are generally smallerthan the cross-sections of the plaque 1801 and the cross-sections of thefat 1803, and the cross-sections of the plaque 1 801 are smaller thanthe cross-sections of the lumen 1802. At some distances from the ostium(for example, from about distances 7 to 9 along the x-axis) one or moreof the cross-sectional areas of the plaque, the lumen, and/or the fatare substantially similar, or nearly the same, although even in theseareas the cross-sections of the lumen 1802 are smaller than thecross-sections of the plaque 1801, and the cross-sections of the plaque1801 are smaller than the cross-sections of the fat 1803. However, atother distances from the ostium the cross-sections differ greatly andthis difference is clearly visible from the plaque cross-section plot1801, the lumen cross-section plot 1802, and the fat cross-section plot1803. For example, in a section of the coronary from about the distance16 (indicated by line “A”) to about the distance 25 (indicated by line“B”) along the x-axis, the cross-section area of the fat 1803 is visiblygreater the cross-section of the plaque 1801, and the cross-section areaof the plaque 1801 is visibly greater than the cross-section area of thelumen 1802. As a general example, at one specific distance “20”indicated by the line “C” the cross-sectional area of the lumen 1802 isabout 9 units, the cross-sectional area of the plaque 1801 is about 18units², and the cross-sectional area of the fat is about 24 units². As aspecific example, at the specific distance 20 mm indicated by the lineC, the cross-sectional area of the lumen 1802 is about 9 mm², thecross-sectional area of the plaque 1801 is about 18 mm², and thecross-sectional area of the fat is about 24 mm². Accordingly, atdistance “20” the ratio of the fat:plaque=1.33, the ratio of thefat:lumen=2.67, and the ratio of the plaque:fat=2.00. In someembodiments, the ratios of one or more of fat:plaque, fat:lumen, and/orplaque:fat are calculated as a plurality of distances in this data canbe provided on a display or in a report as numbers or in a plot of theratios. In an example, ratios that exceed a certain threshold can beflagged for further investigation of the portion or the coronary arterythat corresponds to where the ratio exceeds a certain threshold. Inanother example, ratios that exceed a certain threshold for a certaindistance (portion of the coronary artery) can be flagged for furtherinvestigation of the corresponding portion of the coronary artery. Suchcompartmental ratios may be used to indicate distinct differences inrisks associated with plaque.

Ratios of compartments of the plaque, lumen and fat along a coronaryartery can also be calculated based on summated volume (e.g., based onthe cross-sectional area of the plaque, lumen, and fat over a certaindistance) and the summated volume can be included in a generated displayor in a report. The ratios of compartments of the plaque, lumen and fatalong a coronary artery calculated based on summated volume may beindicative of a patient's risk associated with plaque. In an example, aplurality of portions of the coronary artery can be used to calculate aplurality of summated volumes. In an example, for a point where across-section area ratio (e.g., fat:plaque, fat:lumen, and/orplaque:fat) is exceeds a certain threshold (or is above a certainthreshold for a certain distance), this can mark a starting point tocalculate the summated volume of the coronary artery. In an example, thepoint where cross-sectional area ratio (e.g., fat:plaque, fat:lumen,and/or plaque:fat) falls back below a certain threshold (or falls belowa certain threshold for a certain distance) this can mark and endingpoint to calculate the summated volume of the coronary artery. Anexample using the plots in FIG. 18, at about line A one or more of thecross section area ratios (e.g., fat:plaque, fat:lumen, and/orplaque:fat) may exceed a certain threshold and thus line a can mark astarting point to calculate this made volume of the coronary artery; andthen at about line B one or more of the cross section area ratios (e.g.,fat:plaque, fat:lumen, and/or plaque:fat) may fall below a certainthreshold and thus line B may mark the ending point to calculate thismade volume of the coronary artery. Although the portion of the plotscorresponding to distances 16-25 generally shows the largest ratios, insome examples smaller ratios may it may also indicate thebeginning/ending of summated volume calculations. For example, beginningat a distance 34, the plots of the plaque cross-section 1801, lumencross-section 1802, and fat cross-section 1803 vary consistently(although not greatly) to distance 46, and then again continue to varyconsistently (although not greatly) to distance 56. In some examples,the summated volume may be determined to be calculated for a portion ofthe coronary artery when one or more of the cross section area ratios(e.g., fat:plaque, fat:lumen, and/or plaque:fat) such that they exceed acertain threshold for a certain distance, and the results flagged forfurther investigation of the corresponding part of the coronary arteryeither on a display or in a generated report. Such compartmental ratiosmay be used to indicate distinct differences in risks associated withplaque.

In some implementations, other compartmental ratios (e.g., fat:plaque,fat:lumen, and/or plaque:fat) may be calculated and provided on adisplay, or in a report, to provide additional information about apatient's coronary arteries. Such information may indicate a portion ofan artery for further investigation, or may indicate a condition of apatient. One or more compartmental ratios may be determined at a firstpoint in time (e.g., at a start date), and then determined again at asubsequent (second) point in time (e.g., 2 months later, 6 months later,1 year later, 2 years later, and the like) to track any changes thatoccur in the patient over the theme period. The period of time can be,for example, from 1 week or two weeks, to months or years. In anexample, one or more compartmental ratios may be determined at a firstpoint in time (e.g., when a patient is 50 years old), and thendetermined again that a second point in time (e.g., when a patient is 60years old) to track any changes that occur in the patient over a certaintime period. In an example, for a patient that has a family history ofcoronary artery issues, such testing may be conducted when the patientis 40 years old, and then subsequently every 5 or 10 years to collectinformation on the patient's coronary arteries that may indicate changesto the coronary arteries that may indicate the onset of coronary arteryissues/diseases.

In some implementations, compartmental ratios may be determined forcoronary segments. For example, compartmental ratios of one or more ofthe right coronary artery proximal segment, the right coronary arterymiddle segment, right coronary artery distal segment, a posteriorintraventricular branch of the right coronary artery, the left coronaryartery (main stem), the interior intraventricular branch of the leftcoronary artery, the interior intraventricular branch of the leftcoronary artery middle segment, interior intraventricular branch leftcoronary artery distal segment, the first diagonal branch, the seconddiagonal branch, the circumflex branch of the left coronary arteryproximal segment, the first marginal branch, circumflex branch of theleft coronary artery middle segment, second marginal branch, thecircumflex branch of the left coronary artery distal segment, theposterior left intraventricular branch the right coronary artery, and/orthe intermediate atrial branch for the right coronary artery. In someimplementations, compartmental ratios may be determined for any coronaryvessels. Information relating to compartmental ratios that aredetermined for a patient may be presented on one or more paper orelectronic reports, plots, graphs and the like. In some implementations,two or more compartmental ratios for a particular patient are determinedand presented as a patient summation of compartmental ratios. Forexample, compartmental ratios of two or more portions of a patient'sright coronary artery may be presented and report on a display toindicate the compartmental ratios of these portions of the rightcoronary artery. In some examples, compartmental ratios of all of theportions of the right coronary artery are determined presented forevaluation. In some examples, compartmental ratios of all the portionsof the left coronary artery are determined and presented for evaluation.In some examples, compartmental ratios of corresponding parts of theleft coronary artery and the right coronary artery are determined andpresented for evaluation. Various embodiments of compartmental ratioscan be included with any of the other information described herein toindicates plaque risk.

FIG. 19 is another chart of plots illustrating the compartment areas ofcross-sections of plaque 1901, lumen 1902, and fat 1903 along the lengthof a coronary artery. Similar to FIG. 18, the distances of a measuredcross-section of plaque, lumen, and fat from the ostium of a coronaryartery are plotted on the x-axis, and areas of the respectivecross-section of the plaque are plotted on the y-axis. The coronaryartery in FIG. 19 illustrates an example of different compartmentalratios than the artery in FIG. 18.

Examples of Certain Embodiments

The following are non-limiting examples of certain embodiments ofsystems and methods of characterizing coronary plaque. Other embodimentsmay include one or more other features, or different features, that arediscussed herein.

Embodiment 1: A method for characterization of coronary plaque tissuedata and perivascular tissue data using image data gathered from acomputed tomography (CT) scan along a blood vessel, the imageinformation including radiodensity values of coronary plaque andperivascular tissue located adjacent to the coronary plaque, the methodcomprising: quantifying, in the image data, the radiodensity in regionsof coronary plaque; quantifying, in the image data, radiodensity in atleast one region of corresponding perivascular tissue adjacent to thecoronary plaque; determining gradients of the quantified radiodensityvalues within the coronary plaque and the quantified radiodensity valueswithin the corresponding perivascular tissue; determining a ratio of thequantified radiodensity values within the coronary plaque and thecorresponding perivascular tissue; and characterizing the coronaryplaque by analyzing one or more of: the gradients of the quantifiedradiodensity values in the coronary plaque and the correspondingperivascular tissue, or the ratio of the coronary plaque radiodensityvalues and the radiodensity values of the corresponding perivasculartissue, wherein the method is performed by one or more computer hardwareprocessors configured to execute computer-executable instructions on anon-transitory computer storage medium.

Embodiment 2: The method of embodiment 1, wherein the perivasculartissue comprises at least one of coronary artery lumen, fat, coronaryplaque or myocardium.

Embodiment 3: The method of any one of embodiments 1 and 2, furthercomprising receiving, via a network, the image data at a data storagecomponent.

Embodiment 4: The method of embodiment 3, wherein the network is one ofthe Internet or a wide area network (WAN).

Embodiment 5: The method of any one of embodiments 1-4, wherein theimage data from the CT scan includes at least ten images.

Embodiment 6: The method of any one of embodiments 1-4, wherein theimage data from the CT scan includes at least 30 images.

Embodiment 7: The method of any one of embodiments 1-6, furthercomprising generating a patient report comprising at least one of adiagnosis, a prognosis, or a recommended treatment for a patient basedon the characterization of the coronary plaque.

Embodiment 8: The method of any one of embodiments 1-7, whereinquantifying radiodensity in at least one region of perivascular tissuecomprises quantifying radiodensity, of the scan information, forcoronary plaque and adipose tissue in each of one or more regions orlayers of perivascular tissue.

Embodiment 9: The method of any one of embodiments 1-8, wherein theradiodensity of the scan information is quantified for water in each ofone or more of the regions of coronary plaque and perivascular tissue.

Embodiment 10: The method of any one of embodiments 1-9, whereinradiodensity of the scan information is quantified for low radiodensityplaque in the each of one or more regions or layers of coronary plaque.

Embodiment 11: The method of any one of embodiments 1-10, wherein thecoronary plaque radiodensity values and the perivascular tissueradiodensity values are an average radiodensity.

Embodiment 12: The method of any one of embodiments 1-10, wherein thewherein the coronary plaque radiodensity values and the perivasculartissue radiodensity values are a maximum radiodensity.

Embodiment 13: The method of any one of embodiments 1-10, wherein thewherein the coronary plaque radiodensity values and the perivasculartissue radiodensity values are a minimum radiodensity.

Embodiment 14: The method of any one of embodiments 1-13, wherein thequantified radiodensities are transformed numerical radiodensity valuesof the image data.

Embodiment 15: The method of any one of embodiments 1-14, wherein thequantified radiodensities account for patient- and CT-specificparameters, including one or more of iodinated contrast agent, contrasttype, injection rate, aortic contrast opacification, left ventricularblood pool opacification, signal-to-noise, contrast-to-noise, tubevoltage, milliamps, method of cardiac gating, single and multiple energyimage acquisition, CT scanner type, heart rate, heart rhythm, or bloodpressure.

Embodiment 16: The method of any one of embodiments 1-15, furthercomprising reporting the quantified radiodensities of the coronaryplaque and the perivascular tissue as a gradient.

Embodiment 17: The method of any one of embodiments 1 and 16, whereinthe quantified radiodensities of the coronary plaque and theperivascular tissue are reported as a ratio of the slopes of thegradients from the coronary plaque to perivascular tissue adjacent tothe coronary plaque.

Embodiment 18: The method of any one of embodiments 1-17, wherein thequantified radiodensities of the coronary plaque and the perivasculartissue are reported as the difference in radiodensity values from thecoronary plaque to the perivascular tissue.

Embodiment 19: The method of any one of embodiments 1-18, wherein theimage data is gathered from a CT scan along a length of at least one ofa right coronary artery, left anterior descending artery, leftcircumflex artery, or their branches, or aorta, or carotid arteries, orfemoral arteries, or renal arteries.

Embodiment 20: The method of any one of embodiments 1-18, wherein thedata is gathered from a CT scan along a length of a non-coronaryreference vessel.

Embodiment 21: The method of embodiment 20, wherein the non-coronaryreference vessel is the aorta.

Embodiment 22: The method of any one of embodiments 1-21, wherein theradiodensity is quantified in Hounsfield units.

Embodiment 23: The method of any one of embodiments 1-21, whereinradiodensity is quantified in absolute material densities whenmultienergy CT is performed.

Embodiment 24: The method of any one of embodiments 1-24, wherein one ormore regions or layers of perivascular tissue extend to an end distancefrom the outer wall of the blood vessel, the end distance being thefixed distance where the radiodensity of adipose tissue (i) reaches aminimum value within the scanned anatomical area in a healthy vessel, or(ii) drops by a relative percent (e.g., 10%); or (iii) drops by arelative percent versus a baseline radiodensity value in a vessel of thesame type free of disease.

Embodiment 25: The method of any one of embodiments 1-25, wherein one ormore regions or layers of the coronary plaque extend to an end distancefrom the outer wall of the blood vessel, the end distance being thefixed distance where the radiodensity of adipose tissue (i) reaches amaximum value within the plaque, or (ii) increases by a relative percent(e.g., 10%); (iii) or changes by a relative percent vs the lowestradiodensity value in the plaque.

Embodiment 26: The method of embodiment 24, wherein the baselineradiodensity value is the average radiodensity quantified in a layer ofperivascular tissue lying within a fixed layer or region surrounding theouter vessel wall is measured by a thickness, area or volume.

Embodiment 27: The method of embodiment 24, wherein the baselineperivascular tissue radiodensity is the radiodensity quantified foradipose tissue in a layer of perivascular tissue lying proximal to theouter wall of the blood vessel.

Embodiment 28: The method of embodiment 24, wherein the baselineperivascular tissue radiodensity is the radiodensity quantified forwater in a layer of perivascular tissue lying proximal to the outer wallof the blood vessel.

Embodiment 29: The method of embodiment 24, wherein the baselineradiodensity is an average radiodensity.

Embodiment 30: The method of embodiment 24, wherein the baselineradiodensity is a maximum radiodensity.

Embodiment 31: The method of embodiment 24, wherein the baselineradiodensity is a minimum radiodensity.

Embodiment 32: The method of embodiment 24, wherein the baselineradiodensity value is the average radiodensity quantified in a layer ofcoronary plaque tissue within a fixed layer or region within the plaqueand is measured by a thickness, area or volume.

Embodiment 33: The method of embodiment 24, wherein the baselinecoronary plaque radiodensity is the radiodensity quantified for allcoronary plaques in the measured vessels.

Embodiment 34: The method of any one of embodiments 1-33, furthercomprising: determining a plot of the change in quantified radiodensityrelative to baseline radiodensity in each of one or more concentriclayers of perivascular tissue with respect to distance from the outerwall of the blood vessel up to an end distance; determining the area ofthe region bound by the plot of the change in quantified radiodensityand a plot of baseline radiodensity with respect to distance from theouter wall of the blood vessel up to the end distance; and dividing saidarea by the quantified radiodensity measured at a distance from theouter wall of the blood vessel, wherein the distance is less than theradius of the vessel or is a distance from the outer surface of thevessel above which the quantified radiodensity of adipose tissue dropsby more than 5% compared to the baseline radiodensity of adipose tissuein a vessel of the same type free of disease.

Embodiment 35: The method of any one of embodiments 1-34, furthercomprising: determining a plot of the change in quantified radiodensityrelative to baseline radiodensity in each of one or more concentriclayers of coronary plaque tissue with respect to distance from the outerwall of the blood vessel up to the inner surface of the plaque;determining the area of the region bound by the plot of the change inquantified radiodensity and a plot of baseline radiodensity with respectto distance from the outer wall of the blood vessel up to the innersurface of the plaque; and dividing said area by the quantifiedradiodensity measured at a distance from the outer wall of the bloodvessel, wherein the distance is less than the radius of the vessel or isa distance from the outer surface of the vessel above which thequantified radiodensity of adipose tissue drops by more than 5% comparedto the baseline radiodensity of adipose tissue in a vessel of the sametype free of disease.

Embodiment 36: The method of embodiment 25, wherein the quantifiedradiodensity is the quantified radiodensity of adipose tissue in theeach of one or more regions or layers of perivascular tissue or coronaryplaque.

Embodiment 37: The method of embodiment 25, wherein the quantifiedradiodensity is the quantified radiodensity of water in the each of oneor more regions or layers of perivascular tissue.

Embodiment 38: The method of embodiment 25, wherein the quantifiedradiodensity is an average radiodensity.

Embodiment 39: The method of embodiment 25, wherein the quantifiedradiodensities are a maximum radiodensity.

Embodiment 40: The method of embodiment 25, wherein the perivasculartissue extends to an end distance from the outer wall of the bloodvessel, the end distance being the fixed distance where the radiodensityof adipose tissue (i) reaches a minimum value within the scannedanatomical area in a healthy vessel; (ii) or drops by a relative percent(e.g., 10%); or (iii) drops by a relative percent vs the baselineradiodensity value in a vessel of the same type free of disease.

Embodiment 41: The method of any one of embodiments 1-40, wherein one ormore regions or layers of coronary plaque tissue extend to an enddistance from the outer wall of the blood vessel, the end distance beingthe fixed distance where the radiodensity of adipose tissue (i) reachesa maximum value within the plaque; or (ii) increases by a relativepercent (e.g., 10%); (iii) or changes by a relative percent vs thelowest radiodensity value in the plaque.

Embodiment 42: The method of any one of embodiments 1-41, furthercomprising normalizing the quantified radiodensity of the coronaryplaque and the perivascular tissue to CT scan parameters (patient- andCT-specific parameters), which include one or more of iodinated contrastagent, contrast type, injection rate, aortic contrast opacification,left ventricular blood pool opacification, signal-to-noise,contrast-to-noise, tube voltage, milliamps, method of cardiac gating,single and multiple energy image acquisition, CT scanner type, heartrate, heart rhythm, or blood pressure; normalize the quantifiedradiodensity of the coronary plaque-associated perivascular fat toremote perivascular fat; and normalize the quantified radiodensity ofthe coronary plaque to remote coronary plaques.

Embodiment 43: The method of any one of embodiments 1-42, furthercomprising quantifying other high risk plaque features, such asremodeling, volume, spotty calcifications, and further characterizingthe high risk plaque based on one or more of the high risk plaquefeatures.

Embodiment 44: The method of any one of the preceding embodiments,wherein characterizing the coronary plaque is based in part on plaqueheterogeneity comprising calcium and non-calcified plaque admixtures.

Embodiment 45: The method of any one of the preceding embodiments,wherein characterizing the coronary plaque comprises identifying thecoronary plaque as a high risk plaque if it is prone to be implicated asculprit lesions in future acute coronary events, based on comparisonwith previously classified patient image data.

Embodiment 46: The method of any one of the preceding embodiments,wherein characterizing the coronary plaque comprises identifying thecoronary plaque as a high risk plaque if it is likely to cause ischemiabased on a comparison with previously classified patient image data.

Embodiment 47: The method of any one of the preceding embodiments,wherein characterizing the coronary plaque comprises identifying thecoronary plaque as a high risk plaque if it is likely to cause vasospasmbased on a comparison with previously classified patient image data.

Embodiment 48: The method of any one of the preceding embodiments,wherein characterizing the coronary plaque comprises identifying thecoronary plaque as a high risk plaque if it is likely to rapidlyprogress based on a comparisons with previously classified patient imagedata.

Embodiment 49: The method of any one of the preceding embodiments,wherein characterizing the coronary plaque comprises identifying thecoronary plaque as a high risk plaque if it is likely not to calcify,based on a comparisons with previously classified patient image data.

Embodiment 50: The method of any one of the preceding embodiments,wherein characterizing the coronary plaque comprises identifying thecoronary plaque as a high risk plaque if it is likely not to respond,regress or stabilize to medical therapy based on a comparisons withpreviously classified patient image data.

Embodiment 51: The method of any one of the preceding embodiments,wherein characterizing the coronary plaque comprises identifying thecoronary plaque as a high risk plaque if it is associated withcomplications at the time of revascularization (such as by inducingno-reflow phenomenon) based on a comparisons with previously classifiedpatient image data.

Embodiment 52: A system for volumetric characterization of coronaryplaque tissue data and perivascular tissue data using image datagathered from one or more computed tomography (CT) scans along a bloodvessel, the image information including radiodensity values of coronaryplaque and perivascular tissue located adjacent to the coronary plaque,comprising: a first non-transitory computer storage medium configured toat least store the image data; a second non-transitory computer storagemedium configured to at least store computer-executable instructions;and one or more computer hardware processors in communication with thesecond non-transitory computer storage medium, the one or more computerhardware processors configured to execute the computer-executableinstructions to at least: quantify, in the image data, the radiodensityin regions of coronary plaque; quantify, in the image data, radiodensityin at least one region of corresponding perivascular tissue adjacent tothe coronary plaque; determine gradients of the quantified radiodensityvalues within the coronary plaque and the quantified radiodensity valueswithin the corresponding perivascular tissue; determine a ratio of thequantified radiodensity values within the coronary plaque and thecorresponding perivascular tissue; and characterize the coronary plaqueby analyzing one or more of: the gradients of the quantifiedradiodensity values in the coronary plaque and the correspondingperivascular tissue, or the ratio of the coronary plaque radiodensityvalues and the radiodensity values of the corresponding perivasculartissue.

Embodiment 53: A non-transitory computer readable medium comprisinginstructions that, when executed, cause an apparatus to perform a methodcomprising: quantifying, in the image data, the radiodensity in regionsof coronary plaque; quantifying, in the image data, radiodensity in atleast one region of corresponding perivascular tissue adjacent to thecoronary plaque; determining gradients of the quantified radiodensityvalues within the coronary plaque and the quantified radiodensity valueswithin the corresponding perivascular tissue; determining a ratio of thequantified radiodensity values within the coronary plaque and thecorresponding perivascular tissue; and characterizing the coronaryplaque by analyzing one or more of: the gradients of the quantifiedradiodensity values in the coronary plaque and the correspondingperivascular tissue, or the ratio of the coronary plaque radiodensityvalues and the radiodensity values of the corresponding perivasculartissue.

Embodiment 54: A system comprising a processor and a non-transientstorage medium including processor executable instructions implementinga processing system for characterizing coronary plaque configured to:quantify, in the image data, radiodensity in regions of coronary plaque;quantify, in the image data, radiodensity in at least one region ofcorresponding perivascular tissue adjacent to the coronary plaque;characterize one or more medical conditions based on the quantifiedradiodensity properties of coronary plaque and the radiodensity in atleast one region of corresponding perivascular tissue adjacent to thecoronary plaque using at least one of a ratio of the quantifiedradiodensity values within the coronary plaque and the correspondingperivascular tissue, or at least one gradient of the quantifiedradiodensity values in the coronary plaque and the correspondingperivascular tissue.

Embodiment 55: A non-transitory computer readable medium comprisinginstructions that, when executed, cause an apparatus to perform a methodcomprising: quantifying, in the image data, radiodensity in regions ofcoronary plaque; quantifying, in the image data, radiodensity in atleast one region of corresponding perivascular tissue adjacent to thecoronary plaque; characterizing one or more medical conditions based onthe quantified radiodensity properties of coronary plaque and theradiodensity in at least one region of corresponding perivascular tissueadjacent to the coronary plaque using at least one of a ratio of thequantified radiodensity values within the coronary plaque and thecorresponding perivascular tissue, or at least one gradient of thequantified radiodensity values in the coronary plaque and thecorresponding perivascular tissue.

Implementing Systems and Terminology

Implementations disclosed herein provide systems, methods and apparatusfor mask-less phase detection autofocus. One skilled in the art willrecognize that these embodiments may be implemented in hardware,software, firmware, or any combination thereof.

In some embodiments, the circuits, processes, and systems discussedabove may be utilized in a wireless communication device, for example, amobile wireless device. The wireless communication device may be a kindof electronic device used to wirelessly communicate with otherelectronic devices. Examples of wireless communication devices includecellular telephones, smart phones, Personal Digital Assistants (PDAs),e-readers, gaming systems, music players, netbooks, wireless modems,laptop computers, tablet devices, etc., for example, the mobile wirelessdevice 130C (FIG. 1).

The wireless communication device may include one or more image sensors,one or more image signal processors, and a memory including instructionsor modules for carrying out the process discussed above. The device mayalso have data, a processor loading instructions and/or data frommemory, one or more communication interfaces, one or more input devices,one or more output devices such as a display device and a powersource/interface. The wireless communication device may additionallyinclude a transmitter and a receiver. The transmitter and receiver maybe jointly referred to as a transceiver. The transceiver may be coupledto one or more antennas for transmitting and/or receiving wirelesssignals.

The wireless communication device may wirelessly connect to anotherelectronic device (e.g., base station) to communicate information. Forexample, to communicate information received from the processing system120 (FIG. 1) to/from another device 130 (FIG. 1). A wirelesscommunication device may alternatively be referred to as a mobiledevice, a mobile station, a subscriber station, a user equipment (UE), aremote station, an access terminal, a mobile terminal, a terminal, auser terminal, a subscriber unit, etc. Examples of wirelesscommunication devices include laptop or desktop computers, cellularphones, smart phones, tablet devices, etc. Wireless communicationdevices may operate in accordance with one or more industry standards.Thus, the general term “wireless communication device” or “mobiledevice” may include wireless communication devices described withvarying nomenclatures according to industry standards (e.g., accessterminal, user equipment (UE), remote terminal, etc.).

The functions described herein may be stored as one or more instructionson a processor-readable or computer-readable medium. For example, on theprocessing system 120 or any of the devices 130. The term“computer-readable medium” refers to any available medium that can beaccessed by a computer or processor. By way of example, and notlimitation, such a medium may comprise RAM, ROM, EEPROM, flash memory,CD-ROM or other optical disk storage, magnetic disk storage or othermagnetic storage devices, or any other medium that can be used to storedesired program code in the form of instructions or data structures andthat can be accessed by a computer. Disk and disc, as used herein,includes compact disc (CD), laser disc, optical disc, digital versatiledisc (DVD), floppy disk and Blu-ray® disc where disks usually reproducedata magnetically, while discs reproduce data optically with lasers. Itshould be noted that a computer-readable medium may be tangible andnon-transitory. The term “computer-program product” refers to acomputing device or processor in combination with code or instructions(e.g., a “program”) that may be executed, processed or computed by thecomputing device or processor. As used herein, the term “code” may referto software, instructions, code or data that is/are executable by acomputing device or processor.

The methods disclosed herein comprise one or more steps or actions forachieving the described method. The method steps and/or actions may beinterchanged with one another without departing from the scope of theclaims. In other words, unless a specific order of steps or actions isrequired for proper operation of the method that is being described, theorder and/or use of specific steps and/or actions may be modifiedwithout departing from the scope of the claims.

It should be noted that the terms “couple,” “coupling,” “coupled” orother variations of the word couple as used herein may indicate eitheran indirect connection or a direct connection. For example, if a firstcomponent is “coupled” to a second component, the first component may beeither indirectly connected to the second component or directlyconnected to the second component. As used herein, the term “plurality”denotes two or more. For example, a plurality of components indicatestwo or more components.

The term “determining” encompasses a wide variety of actions and,therefore, “determining” can include calculating, computing, processing,deriving, investigating, looking up (e.g., looking up in a table, adatabase or another data structure), ascertaining and the like. Also,“determining” can include receiving (e.g., receiving information),accessing (e.g., accessing data in a memory) and the like. Also,“determining” can include resolving, selecting, choosing, establishingand the like.

The phrase “based on” does not mean “based only on,” unless expresslyspecified otherwise. In other words, the phrase “based on” describesboth “based only on” and “based at least on.”

In the foregoing description, specific details are given to provide athorough understanding of the examples. However, it will be understoodby one of ordinary skill in the art that the examples may be practicedwithout these specific details. For example, electricalcomponents/devices may be shown in block diagrams in order not toobscure the examples in unnecessary detail. In other instances, suchcomponents, other structures and techniques may be shown in detail tofurther explain the examples. Thus, the present invention is notintended to be limited to the implementations shown herein but is to beaccorded the widest scope consistent with the principles and novelfeatures disclosed herein.

What is claimed is:
 1. A computer-implemented method forcharacterization of coronary plaque by analyzing metrics generated fromradiodensity values of coronary plaque and radiodensity values ofperivascular tissue located adjacent to the coronary plaque of a patientand previously stored metrics generated from radiodensity values ofcoronary plaque and radiodensity values of perivascular tissue locatedadjacent to the coronary plaque of other people, the method comprising:generating image information for the patient, the image informationincluding image data of computed tomography (CT) scans along a vessel ofthe patient, the image data having radiodensity values of coronaryplaque and radiodensity values of perivascular tissue located adjacentto the coronary plaque, wherein generating the image informationincludes accessing CT scan parameters stored for the patient in adatabase, the CT scan parameters including one or more of a CT scannertype, CT scanner tube amperage, or CT scanner peak tube voltage of theCT scanner used to generate the CT scans, one or more of the CT scannernoise, CT scanner signal-to-noise ratio, or CT scanner contrast-to-noiseratio of the CT scanner used to generate the CT scans, one or more ofcontrast type used in the patient for the CT scans, or an injection rateof the contrast into the patient, and one or more of the patient's heartrate or the patient's blood pressure when the CT scans were generated;and automatically determining radiodensity values of coronary plaque andradiodensity values of perivascular tissue in the image accounting forthe CT scan parameters; determining, based on applying a machinelearning algorithm to the image information, coronary plaque informationof the patient, wherein determining the coronary plaque informationcomprises quantifying, using the image information, radiodensity valuesin a region of coronary plaque of the patient, quantifying, using theimage information, radiodensity values in a region of perivasculartissue adjacent to the region of coronary plaque of the patient, andgenerating metrics of coronary plaque of the patient using thequantified radiodensity values in the region of coronary plaque and thequantified radiodensity values in the region of perivascular tissueadjacent to the region of coronary plaque; accessing a database ofcoronary plaque information and characteristics of other people, thecoronary plaque information in the database including metrics generatedfrom radiodensity values of a region of coronary plaque in the otherpeople and radiodensity values of perivascular tissue adjacent to theregion of coronary plaque in the other people, and the characteristicsof the other people including information at least of age, sex, race,diabetes, smoking, and prior coronary artery disease; and characterizingthe coronary plaque information of the patient by comparing the metricsof the coronary plaque information and characteristics of the patient tothe metrics of the coronary plaque information of other people in thedatabase having one or more of the same characteristics, whereincharacterizing the coronary plaque information includes identifying thecoronary plaque as a high risk plaque, wherein the method is performedby one or more computer hardware processors configured to executecomputer-executable instructions on a non-transitory computer storagemedium.
 2. The method of claim 1, wherein generating metrics using thequantified radiodensity values in the region of coronary plaque and thequantified radiodensity values in a region of perivascular tissueadjacent to the region of the patient comprises determining, along aline, a slope value of the radiodensity values of the coronary plaqueand a slope value of the radiodensity values of the perivascular tissueadjacent to the coronary plaque.
 3. The method of claim 2, whereingenerating metrics further comprises determining a ratio of the slopevalue of the radiodensity values of the coronary plaque and a slopevalue of the radiodensity values of the perivascular tissue adjacent tothe coronary plaque.
 4. The method of claim 1, wherein generatingmetrics using the quantified radiodensity values in the region ofcoronary plaque and the quantified radiodensity values in a region ofperivascular tissue adjacent to the region of the patient comprisesgenerating, using the image information, a ratio between quantifiedradiodensity values of the coronary plaque and quantified radiodensityvalues of the corresponding perivascular tissue.
 5. The method of claim1, wherein the perivascular tissue is perivascular fat, and generatingmetrics using the quantified radiodensity values in the region ofcoronary plaque and the quantified radiodensity values in the region ofperivascular tissue adjacent to the region of coronary plaque of thepatient comprises generating a ratio of a density of the coronary plaqueand a density of the perivascular fat.
 6. The method of claim 1, whereinthe perivascular tissue is a coronary artery, and generating metricsusing the quantified radiodensity values in the region of coronaryplaque and the quantified radiodensity values in the region ofperivascular tissue adjacent to the region of coronary plaque of thepatient comprises generating a ratio of a density of the coronary plaqueand a density of the coronary artery.
 7. The method of claim 5, whereingenerating the ratio comprises generating the ratio of a maximumradiodensity value of the coronary plaque and a maximum radiodensityvalue of the perivascular fat.
 8. The method of claim 5, whereingenerating the ratio comprises generating a ratio of a minimumradiodensity value of the coronary plaque and a minimum radiodensityvalue of the perivascular fat.
 9. The method of claim 5, whereingenerating the ratio comprises generating a ratio of a maximumradiodensity value of the coronary plaque and a minimum radiodensityvalue of the perivascular fat.
 10. The method of claim 5, whereingenerating the ratio comprises generating a ratio of a minimumradiodensity value of the coronary plaque and a maximum radiodensityvalue of the perivascular fat.
 11. The method of claim 1, wherein thecharacteristics of the patient comprise information of age, sex, race,blood pressure, diabetes, smoking, and prior coronary artery disease.12. The method of claim 11, wherein the database includes coronaryplaque information and characteristics of numerous other people.
 13. Themethod of claim 1, wherein characterizing the coronary plaque comprisesidentifying the coronary plaque as a high risk plaque if it is likely tocause ischemia based on a comparison of the coronary plaque informationand characteristics of the patient to the coronary plaque informationand characteristics of the other people in the database.
 14. The methodof claim 1, wherein characterizing the coronary plaque comprisesidentifying the coronary plaque as a high risk plaque if it is likely tocause vasospasm based on a comparison of the coronary plaque informationand characteristics of the patient to the coronary plaque informationand characteristics of the other people in the database.
 15. The methodof claim 1, wherein characterizing the coronary plaque comprisesidentifying the coronary plaque as a high risk plaque if it is likely torapidly progress based on a comparison of the coronary plaqueinformation and characteristics of the patient to the coronary plaqueinformation and characteristics of the other people in the database. 16.A system for characterization of coronary plaque tissue data andperivascular tissue data using image data gathered from one or morecomputed tomography (CT) scans along a blood vessel, the image dataincluding radiodensity values of coronary plaque and perivascular tissuelocated adjacent to the coronary plaque, the system comprising: a firstnon-transitory computer storage medium configured to store (i) adatabase of coronary plaque information and characteristics of otherpeople, the coronary plaque information including metrics generated fromradiodensity values in coronary plaque and radiodensity values ofperivascular tissue adjacent to the coronary plaque, and (ii) imageinformation including image data of computed tomography (CT) scans alonga vessel of a patient, the image data having radiodensity values ofcoronary plaque and radiodensity values of perivascular tissue locatedadjacent to the coronary plaque; a second non-transitory computerstorage medium configured to at least store computer-executableinstructions; and one or more computer hardware processors incommunication with the second non-transitory computer storage medium,the one or more computer hardware processors configured to execute thecomputer-executable instructions to: generate the image information andstoring the image information on the first non-transitory computerstorage medium, wherein generating the image information includesaccessing CT scan parameters stored for the patient in a database, theCT scan parameters including one or more of a CT scanner type, CTscanner tube amperage, or CT scanner peak tube voltage of the CT scannerused to generate the CT scans, one or more of the CT scanner noise, CTscanner signal-to-noise ratio, or CT scanner contrast-to-noise ratio ofthe CT scanner used to generate the CT scans, one or more of contrasttype used in the patient for the CT scans, or an injection rate of thecontrast into the patient, and one or more of the patient's heart rateor the patient's blood pressure when the CT scans were generated; andautomatically determining radiodensity values of coronary plaque andradiodensity values of perivascular tissue in the image accounting forthe CT scan parameters; determine, based on applying a machine learningalgorithm to the image information, coronary plaque information of thepatient, wherein determining the coronary plaque information comprisesquantifying, using the image information, radiodensity values in aregion of coronary plaque of the patient, quantifying, using the imageinformation, radiodensity values in a region of perivascular tissueadjacent to the region of coronary plaque of the patient, and generatingmetrics using the quantified radiodensity values in the region ofcoronary plaque and the quantified radiodensity values in the region ofperivascular tissue adjacent to the region of the patient; andcharacterize the coronary plaque information of the patient by comparingthe metrics of the coronary plaque information and characteristics ofthe patient to the metrics of the coronary plaque information andcharacteristics of other people in the database, wherein characterizingthe coronary plaque information of the patient includes identifying thecoronary plaque as a high risk plaque.
 17. The system of claim 16,wherein the one or more computer hardware processors are furtherconfigured to execute the computer-executable instructions to generatethe metrics using the quantified radiodensity values in the region ofcoronary plaque and the quantified radiodensity values in a region ofperivascular tissue adjacent to the region of the patient bydetermining, along a line, a slope value of the radiodensity values ofthe coronary plaque and a slope value of the radiodensity values of theperivascular tissue adjacent to the coronary plaque.
 18. The system ofclaim 16, wherein the one or more computer hardware processors arefurther configured to execute the computer-executable instructions togenerate the metrics by determining a ratio of the slope value of theradiodensity values of the coronary plaque and a slope value of theradiodensity values of the perivascular tissue adjacent to the coronaryplaque.
 19. The system of claim 16, wherein the one or more computerhardware processors are further configured to execute thecomputer-executable instructions to generating the metrics using thequantified radiodensity values in the region of coronary plaque and thequantified radiodensity values in a region of perivascular tissueadjacent to the region of the patient by generating, using the imageinformation, a ratio between quantified radiodensity values of thecoronary plaque and quantified radiodensity values of the correspondingperivascular tissue.
 20. The system of claim 16, wherein theperivascular tissue is perivascular fat, and generating metrics usingthe quantified radiodensity values in the region of coronary plaque andthe quantified radiodensity values in the region of perivascular tissueadjacent to the region of coronary plaque of the patient comprisesgenerating a ratio of a density of the coronary plaque and a density ofthe perivascular fat.
 21. The system of claim 16, wherein theperivascular tissue is a coronary artery, and generating metrics usingthe quantified radiodensity values in the region of coronary plaque andthe quantified radiodensity values in the region of perivascular tissueadjacent to the region of coronary plaque of the patient comprisesgenerating a ratio of a density of the coronary plaque and a density ofthe coronary artery.
 22. The system of claim 21, wherein the ratio isone of a ratio of a maximum radiodensity value of the coronary plaqueand a maximum radiodensity value of the perivascular fat, a ratio of aminimum radiodensity value of the coronary plaque and a minimumradiodensity value of the perivascular fat, a ratio of a maximumradiodensity value of the coronary plaque and a minimum radiodensityvalue of the perivascular fat, or a ratio of a minimum radiodensityvalue of the coronary plaque and a maximum radiodensity value of theperivascular fat.
 23. The system of claim 16, wherein thecharacteristics of the patient comprise information of age, sex, race,blood pressure, diabetes, smoking, and prior coronary artery disease.24. The system of claim 16, wherein the database includes coronaryplaque information and characteristics of thousands of other people. 25.A non-transitory computer readable medium comprising instructions that,when executed by one or more processors, cause an apparatus to perform amethod for characterization of coronary plaque and perivascular tissueusing (i) a database of coronary plaque information and characteristicsof other people, the coronary plaque information including metricsgenerated from radiodensity values in coronary plaque and radiodensityvalues of perivascular tissue adjacent to the coronary plaque, and using(ii) image information including image data of computed tomography (CT)scans along a vessel of a patient, the image data having radiodensityvalues of coronary plaque and radiodensity values of perivascular tissuelocated adjacent to the coronary plaque, the method comprising:generating the image information and storing the image information onthe first non-transitory computer storage medium, wherein generating theimage information includes accessing CT scan parameters stored for thepatient in a database, the CT scan parameters including one or more of aCT scanner type, CT scanner tube amperage, or CT scanner peak tubevoltage of the CT scanner used to generate the CT scans, one or more ofthe CT scanner noise, CT scanner signal-to-noise ratio, or CT scannercontrast-to-noise ratio of the CT scanner used to generate the CT scans,one or more of contrast type used in the patient for the CT scans, or aninjection rate of the contrast into the patient, and one or more of thepatient's heart rate or the patient's blood pressure when the CT scanswere generated; and automatically determining radiodensity values ofcoronary plaque and radiodensity values of perivascular tissue in theimage accounting for the CT scan parameters; determining, based onapplying a machine learning algorithm to the image information, coronaryplaque information of the patient, wherein determining the coronaryplaque information comprises quantifying, using the image information,radiodensity values in a region of coronary plaque of the patient,quantifying, using the image information, radiodensity values in aregion of perivascular tissue adjacent to the region of coronary plaqueof the patient, and generating metrics using the quantified radiodensityvalues in the region of coronary plaque and the quantified radiodensityvalues in the region of perivascular tissue adjacent to the region ofthe patient; and characterizing the coronary plaque information of thepatient by comparing the metrics of the coronary plaque information andcharacteristics of the patient to the metrics of the coronary plaqueinformation and characteristics of other people in the database, whereincharacterizing the coronary plaque information of the patient includesidentifying the coronary plaque as a high risk plaque.
 26. Thenon-transitory computer readable medium of claim 25, wherein generatingmetrics using the quantified radiodensity values in the region ofcoronary plaque and the quantified radiodensity values in a region ofperivascular tissue adjacent to the region of the patient comprisesdetermining, along a line, a slope value of the radiodensity values ofthe coronary plaque and a slope value of the radiodensity values of theperivascular tissue adjacent to the coronary plaque.
 27. Thenon-transitory computer readable medium of claim 26, wherein generatingmetrics further comprises determining a ratio of the slope value of theradiodensity values of the coronary plaque and a slope value of theradiodensity values of the perivascular tissue adjacent to the coronaryplaque.
 28. The non-transitory computer readable medium of claim 26,wherein generating metrics using the quantified radiodensity values inthe region of coronary plaque and the quantified radiodensity values ina region of perivascular tissue adjacent to the region of the patientcomprises generating, using the image information, a ratio betweenquantified radiodensity values of the coronary plaque and quantifiedradiodensity values of the corresponding perivascular tissue.
 29. Thenon-transitory computer readable medium of claim 26, wherein thecharacteristics of the patient and the other people comprise informationof age, sex, race, blood pressure, diabetes, smoking, and prior coronaryartery disease.
 30. The non-transitory computer readable medium of claim26, wherein the database includes coronary plaque information andcharacteristics of numerous other people.